• Research article
  • Open access
  • Published: 04 December 2018

The relationship between learning styles and academic performance in TURKISH physiotherapy students

  • Nursen İlçin   ORCID: orcid.org/0000-0003-0174-8224 1 ,
  • Murat Tomruk 1 ,
  • Sevgi Sevi Yeşilyaprak 1 ,
  • Didem Karadibak 1 &
  • Sema Savcı 1  

BMC Medical Education volume  18 , Article number:  291 ( 2018 ) Cite this article

343k Accesses

39 Citations

4 Altmetric

Metrics details

Learning style refers to the unique ways an individual processes and retains new information and skills. In this study, we aimed to identify the learning styles of Turkish physiotherapy students and investigate the relationship between academic performance and learning style subscale scores in order to determine whether the learning styles of physiotherapy students could influence academic performance.

The learning styles of 184 physiotherapy students were determined using the Grasha-Riechmann Student Learning Style Scales. Cumulative grade point average was accepted as a measure of academic performance. The Kruskal-Wallis test was conducted to compare academic performance among the six learning style groups (Independent, Dependent, Competitive, Collaborative, Avoidant, and Participant).

The most common learning style was Collaborative (34.8%). Academic performance was negatively correlated with Avoidant score ( p  < 0.001, r  = − 0.317) and positively correlated with Participant score ( p  < 0.001, r  = 0.400). The academic performance of the Participant learning style group was significantly higher than that of all the other groups ( p  < 0.003).


Although Turkish physiotherapy students most commonly exhibited a Collaborative learning style, the Participant learning style was associated with significantly higher academic performance. Teaching strategies that encourage more participant-style learning may be effective in increasing academic performance among Turkish physiotherapy students.

Peer Review reports

Learning can be defined as permanent changes in behavior induced by life [ 1 ]. According to experiential learning theory, learning is “the process whereby knowledge is created through the transformation of experience” [ 2 , 3 ].

Facilitating the learning process is the primary aim of teaching [ 4 ]. Understanding the learning behavior of students is considered to be a part of this process [ 5 ]. Therefore, the concept of learning styles has become a popular topic in recent literature, with many theories about learning styles put forward to better understand the dynamic process of learning [ 2 , 3 ].

Learning style refers to an individual’s preferred way of processing new information for efficient learning [ 6 ]. Rita Dunn described the concept of learning style as “a unique way developed by students when he/she was learning new and difficult knowledge” [ 7 ]. Learning style is about how students learn rather than what they learn [ 1 ]. The learning process is different for each individual; even in the same educational environment, learning does not occur in all students at the same level and quality [ 8 ]. Research has shown that individuals exhibit different approaches in the learning process and a single strategy or approach was unable to provide optimal learning conditions for all individuals [ 9 ]. This may be related to students’ different backgrounds, strengths, weaknesses, interests, ambitions, levels of motivation, and approaches to studying [ 10 ]. To improve undergraduate education, educators should become more aware of these diverse approaches [ 11 ]. Learning styles may be useful to help students and educators understand how to improve the way they learn and teach, respectively.

Determining students’ learning styles provides information about their specific preferences. Understanding learning styles can make it easier to create, modify, and develop more efficient curriculum and educational programs. It can also encourage students’ participation in these programs and motivate them to gain professional knowledge [ 9 ]. Therefore, determining learning style is quite valuable in order to achieve more effective learning. Researching learning styles provides data on how students learn and find answers to questions [ 5 ].

Considering the potential problems encountered in the undergraduate education of physiotherapists, determining the learning style of physiotherapy students may enable the development of strategies to improve the learning process [ 12 ]. Studies on learning styles in the field of physiotherapy have mostly been conducted in developed countries such as Canada and Australia [ 13 , 14 ]. A study conducted in Australia examined the learning styles of physiotherapy, occupational therapy, and speech pathology students. The results of this study suggest that optimal learning environment should also be taken into consideration while researching how students learn. The authors also stated that future research was needed to investigate correlations between learning styles, instructional methods, and the academic performance of students in the health professions [ 14 ].

To the best of our knowledge, there are no prior publications in the literature that report Turkish physiotherapy students’ learning styles. Furthermore, previous studies mostly used Kolb’s Learning Style Inventory (LSI), Marshall & Merritts’ LSI, or Honey & Mumford’s Learning Style Questionnaire (LSQ) to assess learning styles [ 5 , 13 , 15 , 16 , 17 , 18 ]. Some of these studies also suggested that learning behavior and styles should be investigated using different inventories [ 5 ]. Moreover, a scale that was indicated as valid and reliable for Turkish population was needed to accurately determine the learning styles of Turkish physiotherapy students. Therefore, we opted to use the Grascha-Riechmann Learning Style Scales (GRLSS) to assess the learning styles of physiotherapy students, which will be a first in the literature.

Learning style preferences are influential in learning and academic achievement, and may explain how students learn [ 19 ]. Previous studies have demonstrated a close association between learning style and academic performance [ 20 , 21 ]. Learning styles have been identified as predictors of academic performance and guides for curriculum design. The aim of this study was to determine whether learning style preferences of physiotherapy students could affect academic performance by identifying the learning styles of Turkish physiotherapy students and assessing the relationship between these learning styles and the students’ academic performance. Since physiotherapy education mainly consists of practice lessons and clinical practice and mostly requires active student participation, we hypothesized that physiotherapy students with a Collaborative learning style according to the GRLSS would have higher academic performance.

A cross-sectional survey design using a convenience sample was used. The study population consisted of 488 physiotherapy students who were officially registered for the 2013–2014 academic year in Dokuz Eylul University (DEU) School of Physical Therapy and Rehabilitation. A minimum sample size of 68 participants was calculated with 95% confidence interval and 80% power by using “Epi Info Statcalc Version 6”. Inclusion criteria were (i) age ≥ 17 years, (ii) official registration in DEU School of Physical Therapy and Rehabilitation for the 2013–2014 academic year, (iii) being a first-, second-, third-, or fourth-year undergraduate student of physiotherapy, (iv) ability to read, write, and understand Turkish, and (v) being willing and able to participate in the study. Exclusion criteria were (i) unwilling to participate in the study, (ii) inability to read, write, and understand Turkish. The questionnaire was distributed to the physiotherapy students in a classroom setting during the final exam week of the academic year. Due to the absence of participants who did not attend final exams and were not actively attending classes (non-attendance students), questionnaires were distributed to 217 students in total.

184 physiotherapy students with a mean ± SD age of 21.52 ± 1.75 years participated in the study. Participants were informed verbally and in writing about the purpose of the study and the survey that would be implemented. A research assistant was available in the classroom to provide assistance if required. Demographic characteristics (age, gender, undergraduate year) comprised the first section of the questionnaire, followed by the GRLSS to assess learning style.

Cumulative grade point average (CGPA) shown on the students’ transcripts was used as the measure of academic performance. The students’ CGPAs at the end of the 2013–2014 academic year were obtained from the records held in the student affairs unit of the DEU School of Physical Therapy and Rehabilitation. CGPA was derived by multiplying the grade point (out of 100) with the credit units for each module or course and then dividing the total sum by the total credit units taken in the program.

The local university ethics committee provided ethical approval and informed consent was obtained from the participants before inclusion. Ethical protocol number was 1432-GOA.

Data collection

Grasha-riechmann student learning style scales.

The GRLSS is a five-point Likert-type scale ( response format: strongly disagree, moderately disagree, undecided, moderately agree, strongly agree ) consisting of 60 items which was designed based on student interviews and survey data [ 22 , 23 ]. In accordance with the response to student attitudes toward learning, classroom activities, teachers and peers, six learning styles were defined [ 24 ]. Learning styles that form subscales are the Independent, Avoidant, Collaborative, Dependent, Competitive, and Participant learning styles [ 24 , 25 ]. The six main styles in the GRLSS are described in Table  1 and the scoring of the GRLSS is shown in Table  2 [ 23 , 24 ]. The GRLSS was adapted to Turkish in 2003 and found to have good reliability [ 25 ] (Table  3 ).

The learning styles of the physiotherapy students in the current study were identified according to GRLSS and the students were grouped based on their predominant (highest scoring) style. The mean and median academic performance values of each group were calculated and the significance of the differences between groups was statistically analyzed.

Statistical analysis

Statistical analyses were performed to compare academic performances among the learning style groups and test the significance of pairwise differences. All data were analyzed using Statistical Package for Social Science software (IBM Corporation, version 20.0 for Windows). Descriptive statistics were summarized as frequencies and percentages for categorical variables. Continuous variables were presented as mean and standard deviation when normally distributed and as median and interquartile range when not normally distributed. Mann-Whitney U test was used for between-group analyses of abnormally distributed variables. The variables were investigated using visual (histograms, probability plots) and analytical methods (Kolmogorov-Smirnov/Shapiro-Wilk test) to determine whether they showed normal distribution. As parameters were not normally distributed, the correlation coefficients and their significance were calculated using Spearman test. Strength of correlation was defined as very weak for r values between 0.00–0.19, weak for r values between 0.20–0.39, moderate for r values between 0.40–0.69, strong for r values between 0.70–0.89, and very strong for r values over 0.90 [ 26 ]. As the academic performance was not normally distributed, the Kruskal-Wallis test was conducted to compare this parameter among the six learning style groups. The Mann-Whitney U Test was performed to test the significance of pairwise differences using Bonferroni correction to adjust for multiple comparisons. An overall 5% type-I error level was used to infer statistical significance ( p  < 0.05).

A total of 217 physiotherapy students were invited to participate in the study. Eighteen students refused to participate. Fifteen surveys were discarded due to missing item responses. As a result, data obtained from 184 students were used for the analyses. Overall response rate was 84.8%.

Demographic characteristics (gender, year) and learning style preferences are presented in Table  4 . The most common learning styles among the physiotherapy students according to the GRLSS were Collaborative (34.8%) and Independent (22.3%). The results of GRLSS subscale scores were given in Table  5 . The highest subscale score was Collaborative (Mean ± SD = 3.57 ± 0.62), while Competitive score was the lowest (Mean ± SD = 2.81 ± 0.69).

A moderate positive correlation between academic performance and Participant score was found (p < 0.001, r = 0.400) . A weak negative correlation was also found between academic performance and Avoidant score (p < 0.001, r = − 0.317) . No other significant correlation between academic performance and subscale scores was found (Table  6 ) .

When students were grouped according to learning styles, between-group (Kruskal-Wallis) analysis showed a significant difference in the academic performance of the groups (p < 0.001). Post-hoc (Mann-Whitney U) analysis revealed significantly higher academic performance in the Participant learning style group compared to all of the other learning style groups (Independent, Avoidant, Collaborative, Dependent, and Competitive) (Table  7 ).

The current study assessed the learning styles of Turkish physiotherapy students, and investigated the relationship between their learning styles and academic performance. The results revealed that the Collaborative learning style was most common among the Turkish physiotherapy students. However, students with Participant learning style had statistically higher academic performance when compared to the others. In addition, we found a positive correlation between Participant score and academic performance of the students, which supports the previous finding, while a negative correlation was found between Avoidant score and academic performance. In the case of physiotherapy students in this study, the emphasis should be on developing Participant and Collaborative learning skills. This might involve providing more class activities, discussions, and group projects.

The physiotherapy program at DEU has a combined case study-based and traditional style curriculum including lectures, tutorials, seminars, case study presentations, and supervised small group clinical practice in the hospital and at other health centers. Learning tasks and assessment methods include individual written examinations, practical examinations, homework and assignments as well as collaborative oral presentation and research projects. In the physiotherapy discipline, clinical practice improves students’ occupational skills and is seen as a crucial part of the teaching process [ 12 , 27 ]. Similarly, the teaching and learning approach at DEU is heavily based on practical training and requires active participation and group work. This could be a reason for the greater preference for Collaborative learning style.

Previous studies have indicated that physiotherapy students prefer abstract learning styles [ 28 ] and have desirable approaches to studying [ 29 ]. Canadian and American physiotherapy students preferred Converger (40 and 37% respectively) or Assimilator (35 and 28% respectively) learning styles [ 13 ]. According to descriptions of the learning style categories in the Kolb LSI, Convergers enjoy learning through activities like homework problems, computer simulations, field trips, and reports and demonstrations presented by others. On the other hand, Assimilators prefer attending lectures, reading textbooks, doing independent research and watching demonstrations by instructors when learning. In our study, Turkish physiotherapy students preferred Collaborative (34.8%) or Independent (22.3%) learning styles. According to GRLSS, Collaboratives prefer lectures with small group discussions and group projects (similar to Assimilators), while Independents prefer self-pace instruction and studying alone (similar to Convergers). Therefore, it can be concluded that learning styles of Canadian, American, and Turkish physiotherapy students are similar to each other.

Katz and Heimann used the Kolb LSI in their study and reported average learning style scores instead of the number of students in each of the four learning styles. They reported Converger as the “average” learning style for physiotherapy students [ 30 ]. In our study, the largest proportion of the physiotherapy students had a Collaborative learning style. Moreover, the average learning style was also Collaborative, with the highest average score.

Competitive learning style was the least frequently preferred (5.4%) by Turkish physiotherapy students in our study. The low preference for Competitive learning style indicates that students were less likely to compete with other students in the class to get a grade. Mountford et al. assessed learning styles of Australian physiotherapy students using Honey & Mumford’s LSQ and found that the Pragmatic learning style was the least preferred. According to LSQ, Pragmatists tend to see problem solving as a chance to rise to a challenge [ 31 ]. Considering that both Competitives and Pragmatists like challenges, the least frequently preferred styles of Australian and Turkish physiotherapy students seem to be similar to each other.

Alsop and Ryan pointed out that “personal awareness of learning styles and confidence in communicating this are first steps to achieve an optimal learning environment” [ 32 ]. According to Kolb’s theory, a preferred learning style affects a person’s problem solving ability [ 13 ]. Wessel et al. also stated that in order to provide students the best learning opportunity, educators must be aware of the learning styles and students’ ability to solve problems [ 13 ]. Indeed, evidence supporting these views can be found in the literature. Previous studies showed that students who were aware of their learning style had improved academic performance [ 33 , 34 ]. Nelson et al. found that college students who were tested on their learning style and were given appropriate education according to their learning style profile achieved higher academic performance than other students [ 33 ]. Linares also investigated learning styles in different health care professions (physiotherapy, occupational therapy, physician assistants, nursing and medical technology) and found a significant relationship between learning style and students’ readiness to undertake self-directed learning [ 15 ]. However, Hess et al. found no association between learning style and problem-solving ability in their study [ 35 ].

While planning this study, we hypothesized that students with a Collaborative learning style would have higher academic performance. Although the Collaborative learning style was the most common, these students did not show significantly higher academic performance. However, students with Participant learning style had statistically higher academic performance when compared to the other learning style groups. Characteristics specific to the Participant learning style are enjoyment from attending and participating in class and interest in class activities and discussions. These students enjoy opportunities to discuss class materials and readings. This may suggest that increasing in-class activities and discussions, which encourage participant-style learning, is needed to increase academic performance. Another approach would be to adapt teaching strategies according to the characteristics of Collaboratives, as they represented the largest body of students. Creating a convenient environment in which students could spend more time sharing and cooperating with their teacher and peers may facilitate collaborative learning, thus enhancing academic performance. Organizing the curriculum to include small group discussions within lectures and incorporate group projects may also be beneficial. As Ford et al. stated, “ Identification teaching profiles could be used to tailor the collaborative structure and content delivery ” [ 36 ].

The most important reason for determining learning style is to create a proper teaching strategy [ 37 , 38 , 39 , 40 ]. However, there seems to be no exact relationship between students’ learning style and the curriculum of a program described in the literature [ 13 ]. Learning style alone is not the only factor that may influence a learning situation. Many factors (educational and cultural context of university, individual awareness, life experience, other learning skills, effect of educator, motivation, etc.) may influence the learning process [ 31 ]. Therefore, expecting a simple relationship between learning style and teaching strategy may not be realistic. Moreover, the review of Pashler et al. showed that there was virtually no evidence that people learn better when teaching style is tailored to match students’ preferred learning style [ 41 ]. Nevertheless, future studies investigating physiotherapy educators’ teaching styles and their association with learning styles and academic performance may elucidate this complex issue.

The major strength of this study is that, to the best of our knowledge, ours is the first study investigating the learning styles of Turkish physiotherapy students with relation to academic performance.

There were some limitations to this study. It should be noted that learning style is a self-reported measure that can change based on experience and the demands of a situation. Therefore, it is subjective and able to provide adaptive behavior [ 42 ]. It should also be kept in mind that the conclusions of this study could be limited due to the cross-sectional design, and respondent bias may be an issue because convenience sampling was used to recruit participants. One possible limitation of the study could be the fact that the three of the scale reliabilities reported for GRLSS was poor.

This study investigated the learning styles of physiotherapy students in only one university (DEU) and this could preclude the generalization of our results. Subsequent studies should include students enrolled in the physiotherapy departments of multiple universities in Turkey to achieve an accurate geographical representation. Moreover, future studies on this topic should be conducted in collaboration with universities in Europe, with which we share a cultural connection.

The results of this study showed that the Collaborative learning style was most common among Turkish physiotherapy students. On the other hand, the physiotherapy students with Participant learning style had significantly higher academic performance than students with other learning styles. Teaching strategies consistent with the unique characteristics of the Participant learning style may be an effective way to increase academic performance of Turkish physiotherapy students. Incorporating more in-class activities and discussions about class material and readings may facilitate Participant learning, thus impacting academic performance positively. Another approach may be to adopt teaching strategies that target the predominant Collaborative learning style. Creating a convenient environment for students to share and cooperate with their teacher and peers and organizing the curriculum to include more small group discussions and group projects may also be supportive. Future studies should investigate physiotherapy educators’ teaching styles and their relations with learning styles and academic performance.


Cumulative Grade Point Average

Dokuz Eylul University

Grascha-Riechmann Learning Style Scales

Learning Style Inventory

Learning Style Questionnaire

Hunt DE. Learning style and student needs: An introduction to conceptual level. In: NASSP, editor. Student learning styles: diagnosing and prescribing programs. Reston: National Association of Secondary School Principals; 1979. p. 27–38.

Google Scholar  

Arthurs JB. A juggling act in the classroom: managing different learning styles. Teach Learn Nurs. 2007;2(1):2–7.

Article   Google Scholar  

Coffield F, Moseley D, Hall E, Ecclestone K. Learning styles and pedagogy in post 16 learning: a systematic and critical review. London: The Learning and Skills Research Centre; 2004.

Ramsden P. Learning to teach in higher education. Oxon: Routledge; 2003.

Book   Google Scholar  

Mountford H, Jones S, Tucker B. Learning styles of entry-level physiotherapy students. Adv Physiother. 2006;8(Suppl 3):128–36.

Huston JL, Huston TL. How learning style and personality type can affect performance. The Health Care Supervisor. 1995;13(Suppl 4):38–45.

Dunn RS, Dunn KJ. Teaching secondary students through their individual learning styles: practical approaches for grades 7–12. New Jersey: Prentice Hall; 1993.

Saban A. Ögrenme-Ögretme Süreci Yeni Teori ve Yaklasimlar. Ankara: Nobel; 2005.

Brown T, Zoghi M, Williams B, Jaberzadeh S, Roller L, Palermo C, McKenna L, Wright C, Baird M, Schneider-Kolsky M. Are learning style preferences of health science students predictive of their attitudes towards e-learning? AJET. 2009;25:4.

Felder RM, Brent R. Understanding student differences. J Eng Educ. 2005;94(Suppl 1):57–72.

Carmo L, Gomes A, Pereira F, Mendes A. Learning styles and problem solving strategies. Proceedings of 3rd E-Learning Conference. 2006:7–8.

Hobbs C, Henley E, Higgs J, Williams V. Clinical education program strategies for challenging times. Focus on Health Professional Education: A Multidisciplinary Journal. 2000;2:7.

Wessel J, Loomis J, Rennie S, Brook P, Hoddinott J, Aherne M. Learning styles and perceived problem-solving ability of students in a baccalaureate physiotherapy programme. Physiother Theor Pr. 1999;15(Suppl 1):17–24.

Brown T, Cosgriff T, French G. Learning style preferences of occupational therapy, physiotherapy and speech pathology students: a comparative study. Int J Allied Health Sci Prac. 2008;6:1–12.

Linares AZ. Learning styles of students and faculty in selected health care professions. J Nurs Educ. 1999;38(Suppl 9):407–14.

Zoghi M, Brown T, Williams B, Roller L, Jaberzadeh S, Palermo C, McKenna L, Wright C, Baird M, Schneider-Kolsky M. Learning style preferences of Australian health science students. J Allied Health. 2010;39(Suppl 2):95–103.

Hauer P, Straub C, Wolf S. Learning styles of allied health students using Kolb's LSI-IIa. J Allied Health. 2005;34(Suppl 3):177–82.

Severiens S, Dam GT. Gender and gender identity differences in learning styles. Educ Psychol-Uk. 1997;17(Suppl 1):79–93.

Yazici HJ. Role of learning style preferences and interactive response systems on student learning outcomes. Int J Inf Oper Manag Educ. 2016;6(Suppl 2):109–34.

Ghazivakili Z, Nia RN, Panahi F, Karimi M, Gholsorkhi H, Ahmadi Z. The role of critical thinking skills and learning styles of university students in their academic performance. J Adv Med Educ Prof. 2014;2(Suppl 3):95–102.

Good JP, Ramos D, D'Amore DC. Learning style preferences and academic success of preclinical allied health students. J Allied Health. 2013;42(Suppl 4):81–90.

Riechmann SW, Grasha AF. A rational approach to developing and assessing the construct validity of a student learning style scales instrument. J Psychol. 1974;87(Suppl 2):213–23.

Grasha AF. Teaching with style: a practical guide to enhancing learning by understanding teaching and learning style. Pittsburgh: Alliance publishers; 1996.

Kumar P, Kumar A, Smart K. Assessing the impact of instructional methods and information technology on student learning styles. Issues in Informing Science and Information Technology. 2004;1:533–44.

Uzuntiryaki E, Bilgin I, Geban O. The effect of learning styles on high school students’ achievement and attitudes in Chemistry 2003. https://eric.ed.gov/?id=ED475483 . Accessed 15 May 2016.

Streiner DL, Norman GR, Cairney J. Health measurement scales: a practical guide to their development and use. Oxford: Oxford University Press; 2014.

Forbes R, Nolan D. Factors associated with patient satisfaction in student-led physiotherapy clinics: a qualitative study. Physiotherapy Theory and Practice. 2018;34(9):705–13.

Barris R, Kielhofner G, Bauer D. Learning preferences, values. and student satisfaction J Allied Health. 1985;14(Suppl 1):13–23.

Van Langenberghe HV. Evaluation of students’ approaches to studying in a problem-based physical therapy curriculum. Phys Ther. 1988;68(Suppl 4):522–7.

Katz N, Heimann N. Learning style of students and practitioners in five health professions. Occup Ther J Res. 1991;11(Suppl 4):238–44.

Honey P, Mumford A. The manual of learning styles. 3rd ed. Maidenhead: Peter Honey; 1992.

Alsop A, Ryan S. Making the most of fieldwork education: a practical approach. California: Springer; 2013.

Nelson B, Dunn R, Griggs SA, Primavera L. Effects of learning style intervention on college students retention and achievement. J Coll Stud Dev. 1993;34:364–9.

Sandmire DA, Boyce PF. Pairing of opposite learning styles among allied health students: effects on collaborative performance. J Allied Health. 2004;33(Suppl 2):156–63.

Hess D, Frantz JM. Understanding the learning styles of undergraduate physiotherapy students. Afr J Health Prof Educ. 2014;6(Suppl 1):45–7.

Ford JH, Robinson JM, Wise ME. Adaptation of the Grasha Riechman student learning style survey and teaching style inventory to assess individual teaching and learning styles in a quality improvement collaborative. BMC Medical Education. 2016;16:252.

Kell C, Rv D. Student learning preferences reflect curricular change. Med Teach. 2002;24(Suppl 1):32–40.

Rappolt S, Tassone M. How rehabilitation therapists gather, evaluate, and implement new knowledge. J Contin. Educ Health. 2002;22(Suppl 3):170–80.

Al Maghraby MA, Alshami AM. Learning style and teaching method preferences of Saudi students of physical therapy. Journal of family & community medicine. 2013;20(Suppl 3):192.

Celik Y, Ceylantekin Y, Kilic I. The evaluation of simulation maket in nursing education and the determination of learning style of students. Int J Health Sci. 2017;11(3):74–9.

Pashler H, McDaniel M, Rohrer D, Bjork R. Learning styles: concepts and evidence. Psychol Sci Public Interest. 2008;9(3):105–1.

Cassidy S. Learning styles: an overview of theories, models, and measures. Educ Psychol-Uk. 2004;24(Suppl 4):419–44.

Download references


The authors like to thank all physiotherapy students who participated in this study.

No funding was obtained for this study.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Author information

Authors and affiliations.

School of Physical Therapy and Rehabilitation, Dokuz Eylul University, 35340, Inciralti, Izmir, Turkey

Nursen İlçin, Murat Tomruk, Sevgi Sevi Yeşilyaprak, Didem Karadibak & Sema Savcı

You can also search for this author in PubMed   Google Scholar


Nİ conducted the literature search for the background of the study, analyzed and interpreted statistical data, and wrote the majority of the article. MT conducted the literature search, collected data for the study, analyzed statistical data, and contributed to writing the article. SSY and DK were involved in study planning, data processing, and revising the article. SS contributed to study design and oversaw the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nursen İlçin .

Ethics declarations

Author’s information.

Nursen İlçin, PT, PhD.

İlçin graduated from Dokuz Eylul University, School of Physical Therapy and Rehabilitation in 1998. She received her Master’s degree in 2002 and PhD in 2009 from Dokuz Eylül University. She is currently a associate professor in Geriatric Physiotherapy Department.

Murat Tomruk, PT, PhD.

Tomruk graduated from the School of Physical Therapy and Rehabilitation at Dokuz Eylul University in 2009. He received his MSci degree in Musculoskeletal Physiotherapy in 2013 and his PhD degree in 2018. His doctorate thesis was about manual therapy. He works as a research assistant at Dokuz Eylul University since 2011.

Sevgi Sevi Yeşilyaprak, PT, PhD.

Sevgi Sevi Yeşilyaprak’s speciality is shoulder rehabilitation. Her primary research interests are orthopaedic and sports injuries of the shoulder, shoulder biomechanics, proprioception, and exercise. She has one active and two completed grants. Yeşilyaprak teaches courses on musculoskeletal physiotherapy including sports physiotherapy, musculoskeletal disorders, therapeutic exercises, exercise prescription, and manual physiotherapy techniques.

Didem Karadibak, PT, PhD.

Karadibak obtained her BS degree in Physiotherapy from Hacettepe University in 1992 and her MS and PhD degrees from the Physical Therapy Program of the Institute of Health and Sciences, Dokuz Eylul University in 1998 and 2003, respectively. She is currently a professor of Cardiopulmonary Rehabilitation in the Dokuz Eylul University School of Physical Therapy and Rehabilitation.

Sema Savcı, PT, PhD.

Savcı obtained her BS degree in Physiotherapy from Hacettepe University in 1988 and her MS and PhD degrees from the Physical Therapy Program of the Institute of Health and Sciences, Hacettepe University in 1990 and 1995, respectively. She is currently a professor and serving as the Head of Cardiopulmonary Rehabilitation in the Dokuz Eylul University School of Physical Therapy and Rehabilitation.

Ethics approval and consent to participate

Written ethical approval was taken from the Dokuz Eylül University’s local ethics committee (approval number 1432-GOA) and written informed consent obtained from all the participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Cite this article.

İlçin, N., Tomruk, M., Yeşilyaprak, S.S. et al. The relationship between learning styles and academic performance in TURKISH physiotherapy students. BMC Med Educ 18 , 291 (2018). https://doi.org/10.1186/s12909-018-1400-2

Download citation

Received : 19 June 2018

Accepted : 22 November 2018

Published : 04 December 2018

DOI : https://doi.org/10.1186/s12909-018-1400-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Learning style
  • Academic performance
  • Physiotherapy

BMC Medical Education

ISSN: 1472-6920

learning styles research articles

  •    Home

“Learning style” was proposed in the study of English as a native language by American scholar Thelen in 1954, and then developed into the study of English as a second language. In China, learning style started to be studied relatively late, which was developed on the basis of referring to the concept of learning style in western educational psychology. Chinese experts in the field of education have devoted themselves to investigating the individual differences of learners. In 1994, studies were carried out on learning style, focusing mainly on its classification, the relationship between learning style and language learning performance, etc. Studies have shown that the learning efficiency of learners can be promoted by providing them with appropriate learning content organization methods according to their different learning styles. These studies are of great significance for future research on the acquisition and teaching of Chinese as a foreign language.

A Review of Research on Learning Style

Share and Cite:

1. Introduction

The traditional school education cannot really teach every child without class and teach them according to their aptitude. In recent years, online education has broken the limitations of time and space, making the best educational resources at your fingertips. The mainstream trend of education is to give students accurate personalized learning guidance. Learning style focuses on students’ personalized learning.

Learning style refers to the way in which learners absorb, process and store new information and master new skills. Natural and habitual, this way will not change with teaching methods or learning content (Reid, 1987) . Since the 1960s, the focus of language teaching has shifted from teachers to students. Accordingly, the research focus of language (especially foreign language) educators and researchers has shifted from teaching methods and processes to the language learning process and the characteristics of language learners themselves. Therefore, the individual differences of learners have increasingly attracted the attention of researchers studying second language (L2) acquisition (Li, 2021) . The academic community has basically reached a consensus that the learning efficiency of learners can be improved by offering them proper learning content organization methods according to their different learning styles and guiding their personalized learning styles during learning. Research on learning styles in foreign countries began early. Nearly 70 years have passed since American scholar Thelen (1954) put forward the term “learning style”. In this paper, relevant research studies in different countries were taken as main reference materials, including the research results of L2 acquisition, English teaching, psychology and teaching Chinese as a foreign language (TCFL). Besides, studies on the learning style of learners were sorted out. In addition, a summary was made of previous research results and the consensus reached, providing some references for future research on language acquisition and teaching.

2. Definition and Theoretical Basis of Learning Style

2.1. Definition of Learning Style

Opinions on the definition of learning style vary. At present, it has no unified concept. Reid (2002) , an influential foreign scholar, believed that learning style is a natural and habitual method and skill of personal preference for learners to absorb, process and store new information and master new skills. In China, Tan (1995) provided a generally accepted definition of learning style: Learning style is a study method a learner uses consistently with characteristics of personality and the summation of learning strategy and inclination. After research, it is found that learning style has a consistent definition despite lacking a unified concept in the academic circle. First, learning style is the learning way of learners with personal habits and preferences in the learning process. Second, it is formed by individuals in their long-term study life, with strong stability. The learning style of everyone is different and unique due to the influence of the environment, culture and other factors.

In the current research, learning and cognitive styles as well as learning strategy are usually used as synonyms. The study of learning style was later than and drew on that of cognitive style. Also known as the cognitive approach, cognitive style refers to the habitual way that individuals often adopt in their cognitive process. Specifically, it is the attitudes and ways that individuals prefer and get habituated to during the process of perception, memory, thinking and problem-solving (Song, Li, & Wang, 2001) . Cognitive style primarily studies the way of information processing, while learning style focuses on the differences in the intelligence, emotion, motivation and other aspects of learners, and their preferences for learning environments, content, strategies, etc. To some extent, cognitive style is a significant component of learning style.

Learning strategy refers to the actions taken by learners to facilitate the acquisition, storage, extraction and utilization of information. In plain terms, it is the methods or behaviors of learners to promote learning and make learning faster and more effective (Jiang, 2000) . Learning strategy can be developed through practice and generally change with learning objects and subjects as well as changes in environmental conditions, showing greater flexibility. However, learning style originates from the personality of learners which is different from that of others and has a certain degree of heredity. Moreover, it is gradually formed in long-term learning activities and seldom changes with the change of learning environments and content, with stability (Chen, 2016) .

2.2. Related Teaching Theories

Affiliated with educational psychology, learning style has great guiding significance to educational practice. In the 1630s, Czech educator Comenius put forward the class teaching system in his work Magna Didactica , and initiated the teacher-centered teaching theory. As emphasized by the theory, teachers are the center of teaching activities and take charge of organizing and monitoring the whole process of teaching activities, while students are the objects of knowledge infusion. This learning theory occupied an important position in the realm of education for a time. By the 20 th century, the behaviorist learning theory was affected by the cognitive learning theory holding that students are the subjects of information processing. As a major branch of the cognitive learning theory, the constructivism learning theory proposes that knowledge is acquired through learning, others’ help, information query and meaning construction in a specific environment. The construction of meaning is the ultimate goal of this learning process. The constructivism learning theory is conducive to pushing forward the development of student-centered teaching.

Originating in the 1950s, the individualism learning theory underlines that students are the center of teaching. It also maintains that teachers should help students discover their potential in the teaching process and enable them to teach themselves. Additionally, teachers should advocate meaningful and experiential learning, and require students to take responsibility for themselves, set up scenarios, select materials, raise questions, determine progress and pay attention to results by themselves (Yue, 2015) .

In his monograph Personalized Teaching Theory , Professor Deng East China Normal University put forward the characteristics of personalized teaching in seven aspects: media technology, learning pace, methods, content and objectives as well as evaluation methods and criteria. For instance, learners can learn at their own pace and choose different media technologies and diversified learning strategies and contents. The diversity of learning objectives can adapt to the individual differences of students. Learning tasks, evaluation criteria, etc. can be freely selected. Further, the research equated personalized teaching with adaptive teaching and differentiated teaching with inclusive teaching.

2.3. Related Psychological Theories

Sweller, a famous Australian educational psychologist, and other scholars proposed the cognitive load theory in the 1980s. In light of the theory, people have limited cognitive resources in the cognitive process. A high cognitive load will be brought to learners if the resources to be occupied in a link of information processing exceed the total amount of cognitive resources owned by learners per se, thereby influencing the learning outcomes of learners.

“Schema” has already appeared in the philosophical works of Kant. In 1932, psychologist Bartlett formally brought up the concept of “schema” in psychology and formed a quite systematic schema theory referring to the theory of knowledge representation and storage mode organized on a topic. In brief, it is necessary for people to learn and master a lot of knowledge in their life. Such knowledge is not stored randomly in the brain but divided according to different topics. The related contents under the same topic constitute a knowledge unit which is a schema. Knowledge is schematically stored in long-term memory, which thus reduces the cognitive load of learners.

As a cognitive theory, the dual coding theory was formally put forward in the book Imagery and Verbal Processes in 1971, with a central assumption that verbal and imagery information is stored separately in the long-term memory of people. The theory states that people possess separate visual and auditory processing channels where respective cognitive resources are also independent of each other. The separate visual image channel is used to process materials of visual representation such as videos, pictures, animations and texts, while the separate auditory/verbal one is utilized to process materials of auditory representation like voice commentaries and background music. Learning efficiency can be better improved when people process information through two independent channels, which is more in line with the characteristics of human information processing. The limited capacity hypothesis holds that people’s visual and auditory processing channels are limited in information capacity and unable to present too much information simultaneously. Otherwise, it will lead to information overload and hence affect learning outcomes.

3. Related Research on Learning Styles in China and Abroad

3.1. Foreign Research Results

3.1.1. Elements of Learning Style

The elements of learning style are classified into different types, which are directly related to the classification and measurement of learning style. The most representative elements of learning style have the following several explanations.

The elements of learning style were divided by the Dunns into five categories: environmental, emotional, social, physiological and psychological elements, with a total of 27 specific elements. Keefe (1979) claimed that learning style is composed of 32 elements in cognitive, emotional and physiological categories.

By combining the characteristics of the educational system and culture in China, Chinese scholar Tan pointed out the inappropriateness of western research on the elements of learning style in China. Apart from that, he segmented learning style into 23 elements in physiological, psychological and social categories, and conducted detailed research on them in On Learning Style . Furthermore, the scholar categorized the measurement of the elements of learning style into comprehensive and individual measurements. Comprehensive measurement means that a set of test scales measure multiple elements, which is characterized by strong comprehensiveness. Representative scales are the Learning Style Inventory (LSI) of the Dunns and the Learning Style Profile (LSP) of the National Association of Secondary School Principals. Individual measurement carries out analysis on physiological, psychological and social elements, among which psychological elements mainly include cognitive and emotion-conation factors, and social elements mainly contain personality types and gender perspectives.

Tan summarized the elements of learning style in Learning Style as follows: Physiological elements chiefly comprise intuitive response, brain function and learning time, sound, light, temperature as well as mobility and sitting posture preferences. The learning styles corresponding to learning time preference are principally morning, forenoon, afternoon and evening types. The learning styles corresponding to perceptual response are mainly visual, auditory and kinesthetic types. The learning styles corresponding to sound preference primarily include the need for silence, the use of background sound to mask the interference of other sounds during learning and the tolerance of a certain degree of noise. The learning styles corresponding to light preference are mainly stronger and darker light preferences. Psychological elements largely consist of cognitive, emotional and conative elements as well as psychological development. Social elements are made up of personality types, gender perspectives, etc., including 16 personality types.

3.1.2. Classification of Learning Style

From the 1950s to the present, learning style theory models have developed into more than 70 types, whose specific types are listed in Table 1 .

In the 1990s, Felder & Silerman co-created Feler-Silverman Learning Style Model. In 1991, Felder cooperated with Solomon to design the Felder-Soloman Index of Learning Style (ILS) which mainly measures the situation of learners in the four dimensions of information input, perception, processing and understanding.

Compared with other questionnaires of the same type, this questionnaire has a more reasonable classification and measures the learning styles of learners more scientifically. Index of Learning Style (ILS) is listed in Table 2 .

Table 1 . Table of learning style theory models.

Table 2 . Table of Index of Learning Style (ILS).

3.1.3. Relationship between Learning Style and Language Acquisition

Kogan is the first person to apply the cognitive style to language teaching and published the paper Cognitive Style and Reading Performance in 1980. In his view, “compensation strategies can be better sought by studying the cognitive style of individuals in order to overcome the possible obstacles encountered in reading.” Chen & Wu explored the learning effects of learners who had visual and verbal cognitive styles and learned three different types of teaching videos (classroom record, three split screens and picture-in-picture types) in an online teaching environment. The results show that three kinds of teaching videos had no significant effect on the academic performance of verbal and visual learners. However, verbal learners paid more sustained attention to learning teaching videos than visual ones. In addition, the cognitive load generated by visual learners was significantly higher than that generated by verbal learners in the learning of picture-in-picture teaching videos. Chen & Sun confirmed that multimedia materials containing videos and animations are more suitable for visual learners than those containing texts and animations. In contrast to learners with visual preference, those with verbal preference generate lower cognitive load when learning teaching videos continuously presenting teacher images compared with briefly presenting teacher images. Horner et al. found that learners with a low visual preference would produce a higher cognitive load when learning teaching videos with teacher images, whereas those with a higher visual preference would produce a higher cognitive load when learning teaching videos without teacher images.

3.2. Related Research in the Field of Chinese

In China, the early research on learning style mostly discussed its theoretical definition. Learning style has been lacking a unified definition for a long time, whose definition varies by research angle. Most domestic scholars agree with or cite the definition provided by Tan that learning style refers to the preferences of learners with personality characteristics for methods, means, learning content and environments to complete learning tasks. The research on Chinese includes that of Chinese as a first language on the one hand and L2 on the other hand.

3.2.1. Related Research on Chinese as a First Language

The cognitive style was first applied in Chinese teaching in China from the mid and late 1980s. In Influence of Field Dependence on the Effects of Centralized and Decentralized Literacy , Zhang and Feng (1985) conducted experimental research and reached the following conclusion: “field-independent children are suitable for centralized literacy, while field-dependent ones are suitable for decentralized literacy. Children in between show no significant difference between the two teaching methods.” In A Study of the Relationship between Cognitive Style and Language Learning Strategies , Yao (2006) claimed that learners with different cognitive styles should adopt different language learning strategies. Through the study of learning strategies, it was found that the use and frequency of learning strategies by language learners have a great impact on the learning effect. The study of learning strategies is inseparable from that of cognitive styles. This is because learners can adopt different learning strategies according to their advantages or disadvantages and by understanding their cognitive styles with the aim of improving their language learning ability.

Currently, the domestic research on the relationship between cognitive style and foreign language learning mainly focuses on college English teaching. Zhu et al. employed the Perceptual Learning Style Preference Questionnaire (PLSPQ) developed by Reid and classified 133 students from six classes in a senior high school into six groups of subjects with different learning styles through the experimental method. It was confirmed that the kinesthetic learning style of senior high school students at different levels has the strongest correlation with English learning performance. Wang et al. applied Kolb’s Learning Style Model to verify the significant impact of learning style on fluency in L2 tasks. Zhao made use of Reid’s PLSPQ to test the English acquisition level of college students. Experiments show that students of higher vocational colleges under the Sino-foreign cooperative education model have different learning style preferences and students with different English levels are different in learning style.

Little research has been done on Chinese preschool children. Although some studies have investigated the bilingual education and cognitive style of preschool children (Wang, 2001) , few have explored the relationship between the cognitive style and the L2 acquisition process of children. Through observing and studying 23 preschool children, Li and Ju probed into the correlations of cognitive style with their L2 learning and classroom performance. The results show that the cognitive style of preschool children has an impact on their performance in L2 class despite being not directly associated with their L2 test performance. Meanwhile, field-independent children tend to be better than field-dependent ones in L2 test performance.

Shi (2003) of Chung Yuan Christian University took 122 freshmen as research objects to examine the influence of different learning styles and methods on the learning outcomes of these students in an online learning environment and studied the interactive effects of learning methods on learning outcomes. It was discovered that learning styles and methods exert an influence on learning outcomes.

Different types of cognitive styles will have a certain impact on the learning effects of learners, which however should not be related to the intelligence of learners. Witkin’s Field Cognitive Style Scale represents the measurement and classification of intelligence rather than cognitive style to some degree. In addition, it has been shown that field independence increases with age. Li and Che (2006) revised the verbal-imagery sub-scale in the cognitive style analysis (CSA) system, and analyzed and compared the differences in cognitive style between Chinese and British college students. The results show that Chinese college students prefer the verbal side in the verbal-imagery dimension and the analytic side in the wholist-analytic dimension. Bao et al. (2012) questioned the cognitive style of verbal-imagery division and further distinguished the imagery cognitive style model, thus advancing the research and development of the object-spatial imagery and verbal cognitive style model. Wu (2011) adopted the dividing standards of object-spatial imagery and verbal cognitive style to study and discuss the relationship among the spatial ability, cognitive style and mathematics learning effect of senior high school students. It was discovered that spatial imagery learners have strong abilities in spatial orientation, rotation and visualization, and can achieve better results in mathematics tests. Additionally, male students with a strong tendency towards verbal learning are more likely to achieve better results in spatial orientation ability than female ones. The above analysis shows that different conclusions will be reached according to the classification of different cognitive styles. As a result, more scientific experiments are needed to support the influence of the dividing standards of cognitive style on the learning effects of different learners.

3.2.2. Related Research on Chinese as L2

Few domestic studies have focused on the learning style of Chinese as L2, most of which are based on questionnaires by foreign scholars, and modified and investigated according to the actual situation of teaching and students. Since the second half of the 1990s, especially after 2000, a growing number of researchers had begun to attach importance to the individual differences of students in the research of L2 teaching. Cognitive style, an important part of the individual differences of students, is extensively applied in the study of Chinese teaching. During this period, Wang, Xu & Wang et al. were rather influential in the independent research of cognitive style in the domain of Chinese teaching.

Wang (2006) included the paper Research on Learners Learning Chinese as a Second Language and Cognitive Style in the book Research on Learners Learning Chinese as a Second Language and Cognitive Style . The content involved research on the cognition of Chinese phonetics, characters and vocabulary, the individual differences of learners, etc.

Xu (2006) expressed his opinion in the article Research on the Differences in the Learning Strategies of Chinese Learners with Different Cognitive Styles . From his perspective, the significance of studying cognitive style is that “cognitive style varies from individual to individual. If learning about the cognitive characteristics of learning objects, teachers can formulate corresponding teaching plans and try to take teaching approaches matching the personality characteristics of learners. During group learning, teachers should properly take into account the personality of learners and mobilize their respective strengths to realize mutual complementarity and render the style of teaching and learning as harmonious as possible”. Wang (2009) believed that “cognitive style is a critical individual difference variable. Putting forward the strategy of TCFL based on the cognitive style theory through exploring the relationship between cognitive style and L2 acquisition is beneficial to optimizing the process of TCFL and improving the implementation quality of the TCF course.”

The research on the application of cognitive and learning styles in Chinese teaching greatly enriches the theory and practice of Chinese teaching and lays a good foundation for the further discussion of cognitive/learning styles in Chinese teaching in the future. Based on Reid’s PLSPQ, Yi and Yan (2009) carried out a study on the perceptual style of 325 foreign students from Central Asia. It was found that the Chinese learning style of Central Asian students presents the following overall tendency: tactile > visual > group > individual > auditory, and an obvious tendency in tactile, visual and group types. Fang (2013) conducted a survey on 90 Thai learners from Thailand and Shanghai and discovered that auditory > tactile > kinesthetic > visual in terms of sensory learning style and group > individual in terms of social learning style. Zhao (2016) combined this with the actual situation of Chinese teaching in the Philippines and examined the learning style of 440 students in Philippine public secondary schools. The research showed that the learning style of students in Philippine public secondary schools presents the following tendency: group > visual > kinesthetic > auditory > tactile > individual and perceptual learning style shows no significant difference in age and gender. Ye (2017) conducted an investigation on 156 high school students in Italian high schools, and noticed that the learning style of Italian high school students shows the following tendency: kinesthetic > tactile > auditory > visual.

In addition, some scholars used the questionnaire designed by Oxford et al. as a measurement tool for research. Chen (2015) surveyed 85 overseas students from the United States and found that the visual learning style was preferred by the surveyed student most, followed by auditory and kinesthetic learning styles successively. Yang (2016) drew on the design thought of Wang (2014) based on this questionnaire and investigated 74 middle school students from Burma. Statistics show that the Chinese learning style of these middle school students presents the following tendency: visual > auditory > kinesthetic. Wei (2012) took the learning style questionnaire prepared by Xi’an Jiaotong University as the basis, referred to the questionnaires of Reid & Oxford, and investigated the learning style of 62 South Korean students in Shandong Province. The results show that South Korean students exhibit the following learning style tendency on the whole: auditory > kinesthetic > visual, and use auditory learning style as the main learning style. Li (2014) referred to the English learning style questionnaire compiled by Liu & Dai and the learning style questionnaires of Oxford & Reid. Moreover, the specific situation of Chinese teaching for Russian students in Shandong Province was combined to study the Chinese learning style of 150 Russian students. The conclusions drawn are as follows: Russian students show an obvious tendency towards tactile and visual learning styles; Russian students of different genders show significant differences in auditory and visual learning styles, while those of different ages show significant differences in cooperative and individual learning styles.

Studies on the correlation between learning style and academic performance have obtained abundant research results and conclusions. A large number of empirical studies have proved that a certain correlation exists between academic performance and learning style. Many scholars, including Wang and Xu (2005) , Lu (2005) , Yao, Yan and Liu (2011) , Song and Wang (2012) , Lu, Liu and Xia (2016) , and Zhang (2019) , etc., looked into different experimental objects. The results all show that learning style is significantly associated with academic performance.

4. Conclusion and Research Prospect

4.1. Results of Research on Learning Style

From the perspective of theoretical research, the development of learning style research in recent decades has received attention from multiple disciplines. Plenty of achievements have been attained in not only linguistics but also psychology. The vast majority of domestic and foreign studies on first language and L2 suggest that: How learners absorb, process and store new information and grasp new skills is natural and habitual, and will not change because of different teaching methods or learning contents. Concomitant vocabulary acquisition will be affected by learners’ L2 level, vocabulary size and word-guessing ability, the number of occurrences of target words, reading tasks, tools, etc. Theories related to teaching and psychology have facilitated the deepening of learning style theories. Nevertheless, the results of research on learning style have not yet been applied to language teaching and acquisition as well as psychological research.

4.2. Future Research Prospects

The study of “learning style” has been developing for decades. Where will it go from here? Simply repeating the classification of learning style and merely investigating its influence factors have been unable to meet current research development. Increasing researchers feel that the learning style of learners plays an increasingly important role in both classroom language teaching and daily language acquisition. For this reason, future learning style research should focus on how to put learning style research into the context of language teaching and acquisition for examination and make a combination of learning style, deep language learning, language teaching and other aspects for discussion.

On the other hand, after investigation and research, we learned from consulting front-line Chinese teachers that, scores of different popular language learning methods are currently suitable for young L2 learners, like learning Chinese through singing Chinese songs, watching films, television dramas and variety shows, cooking Chinese food… In future practice, the following questions can be addressed: How about L2 acquisition through these listening, speaking and other channels? Are these channels appropriate for all L2 learners with different learning styles? How to match multi-channel L2 acquisition with learning style?

Apart from that, Chinese research still has room for further development despite some consensus reached by previous studies. For instance, overseas Chinese students remain the largest number among people learning Chinese around the world at present (Li, 2018) . Is the learning style of Chinese learners different from that of European, American, Japanese and Korean learners? Prior research rarely touches upon this aspect. Therefore, it is necessary to conduct related research in more detail and more deeply in the future.


The authors would like to thank International College of Education, Inner Mongolia University.

This research was supported by Inner Mongolia Philosophy and Social Science Planning Project (Grant No. 2021NDC158).

Conflicts of Interest

The author declares no conflicts of interest regarding the publication of this paper.

  •   Articles
  •   Archive
  •   Indexing
  •   Aims & Scope
  •   Editorial Board
  •   For Authors
  •   Publication Fees

Journals Menu  

  • Open Special Issues
  • Published Special Issues
  • Special Issues Guideline
  • E-Mail Alert
  • OJML Subscription
  • Publication Ethics & OA Statement
  • Frequently Asked Questions
  • Recommend to Peers
  • Recommend to Library
  • History Issue

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License .

  • Journals A-Z


  • Publication Fees
  • For Authors
  • Peer-Review Issues
  • Special Issues
  • Manuscript Tracking System
  • Subscription
  • Translation & Proofreading
  • Volume & Issue
  • Open Access
  • Publication Ethics
  • Preservation
  • Privacy Policy

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Am J Pharm Educ
  • v.73(1); 2009 Feb 19

Learning Styles: A Review of Theory, Application, and Best Practices

Much pedagogical research has focused on the concept of “learning styles.” Several authors have proposed that the ability to typify student learning styles can augment the educational experience. As such, instructors might tailor their teaching style so that it is more congruent with a given student's or class of students' learning style. Others have argued that a learning/teaching style mismatch encourages and challenges students to expand their academic capabilities. Best practice might involve offering courses that employ a variety of teaching styles. Several scales are available for the standardization of learning styles. These scales employ a variety of learning style descriptors and are sometimes criticized as being measures of personality rather than learning style. Learning styles may become an increasingly relevant pedagogic concept as classes increase in size and diversity. This review will describe various learning style instruments as well as their potential use and limitations. Also discussed is the use of learning style theory in various concentrations including pharmacy.


The diversity of students engaged in higher education continues to expand. Students come to colleges with varied ethnic and cultural backgrounds, from a multitude of training programs and institutions, and with differing learning styles. 1 Coupled with this increase in diversification has been a growth in distance education programs and expansions in the types of instructional media used to deliver information. 2 , 3 These changes and advances in technology have led many educators to reconsider traditional, uniform instruction methods and stress the importance of considering student learning styles in the design and delivery of course content. 4 - 5 Mismatches between an instructor's style of teaching and a student's method of learning have been cited as potential learning obstacles within the classroom and as a reason for using a variety of teaching modalities to deliver instruction. 6 - 8 The concept of using a menu of teaching modalities is based on the premise that at least some content will be presented in a manner suited to every type of learner within a given classroom or course. Some research has focused on profiling learning types so that instructors have a better understanding of the cohort of students they are educating. 7 - 8 This information can be used to guide the selection of instruction modalities employed in the classroom. Limited research has also focused on describing and characterizing composite learning styles and patterns for students in various concentrations of study (eg, medicine, engineering). 5 , 6 , 9 This review will describe the potential utility and limitations in assessing learning styles.


A benchmark definition of “learning styles” is “characteristic cognitive, effective, and psychosocial behaviors that serve as relatively stable indicators of how learners perceive, interact with, and respond to the learning environment. 10 Learning styles are considered by many to be one factor of success in higher education. Confounding research and, in many instances, application of learning style theory has begat the myriad of methods used to categorize learning styles. No single commonly accepted method currently exists, but alternatively several potential scales and classifications are in use. Most of these scales and classifications are more similar than dissimilar and focus on environmental preferences, sensory modalities, personality types, and/or cognitive styles. 11 Lack of a conceptual framework for both learning style theory and measurement is a common and central criticism in this area. In 2004 the United Kingdom Learning and Skills Research Center commissioned a report intended to systematically examine existing learning style models and instruments. In the commission report, Coffield et al identified several inconsistencies in learning style models and instruments and cautioned educators with regards to their use. 12 The authors also outlined a suggested research agenda for this area.

Alternatively, many researchers have argued that knowledge of learning styles can be of use to both educators and students. Faculty members with knowledge of learning styles can tailor pedagogy so that it best coincides with learning styles exhibited by the majority of students. 4 Alternatively, students with knowledge of their own preferences are empowered to use various techniques to enhance learning, which in turn may impact overall educational satisfaction. This ability is particularly critical and useful when an instructor's teaching style does not match a student's learning style. Compounding the issue of learning styles in the classroom has been the movement in many collegiate environments to distance and/or asynchronous education. 2 , 3 This shift in educational modality is inconsistent with the learning models with which most older students and adult learners are accustomed from their primary and high school education. 3 , 13 , 14 Alternatively, environmental influences and more widespread availability of technological advances (eg, personal digital assistants, digital video, the World Wide Web, wireless Internet) may make younger generations of students more comfortable with distance learning. 15 - 17


As previously stated, several models and measures of learning styles have been described in the literature. Kolb proposed a model involving a 4-stage cyclic structure that begins with a concrete experience, which lends to a reflective observation and subsequently an abstract conceptualization that allows for active experimentation. 18 Kolb's model is associated with the Learning Style Inventory instrument (LSI). The LSI focuses on learner's preferences in terms of concrete versus abstract, and action versus reflection. Learners are subsequently described as divergers, convergers, assimilators, or accommodators.

Honey and Mumford developed an alternative instrument known as the Learning Style Questionnaire (LSQ). 6 Presumably, the LSQ has improved validity and predictive accuracy compared to the LSI. The LSQ describes 4 distinct types of learners: activists (learn primarily by experience), reflectors (learn from reflective observation), theorists (learn from exploring associations and interrelationships), and pragmatics (learn from doing or trying things with practical outcomes). The LSQ has been more widely used and studied in management and business settings and its applicability to academia has been questioned. 6 An alternative to the LSQ, the Canfield Learning Style Inventory (CLSI) describes learning styles along 4 dimensions. 19 These dimensions include conditions for learning, area of interest, mode of learning, and conditions for performance. Analogous to the LSQ, applicability of the CLSI to academic settings has been questioned. Additionally, some confusion surrounding scoring and interpretation of certain result values also exists.

Felder and Silverman introduced a learning style assessment instrument that was specifically designed for classroom use and was first applied in the context of engineering education. 20 The instrument consists of 44 short items with a choice between 2 responses to each sentence. Learners are categorized in 4 dichotomous areas: preference in terms of type and mode of information perception (sensory or intuitive; visual or verbal), approaches to organizing and processing information (active or reflective), and the rate at which students progress towards understanding (sequential or global). The instrument associated with the model is known as the Index of Learning Survey (ILS). 21 The ILS is based on a 44-item questionnaire and outputs a preference profile for a student or an entire class. The preference profile is based on the 4 previously defined learning dimensions. The ILS has several advantages over other instruments including conciseness and ease of administration (in both a written and computerized format). 20 , 21 No published data exist with regards to the use of the ILS in populations of pharmacy students or pharmacists. Cook described a study designed to examine the reliability of the ILS for determining learning styles among a population of internal medicine residents. 20 The researchers administered the ILS twice and the Learning Style Type Indicator (LSTI) once to 138 residents (86 men, 52 women). The LSTI has been previously compared to the ILS by several investigators. 8 , 19 Cook found that the Cronbach's alpha scores for the ILS and LSTI ranged from 0.19 to 0.69. They preliminarily concluded that the ILS scores were reliable and valid among this cohort of residents, particularly within the active-reflective and sensing-intuitive domains. In a separate study, Cook et al attempted to evaluate convergence and discrimination among the ILS, LSI, and another computer-based instrument known as the Cognitive Styles Analysis (CSA). 11 The cohort studied consisted of family medicine and internal medicine residents as well as first- and third-year medical students. Eighty-nine participants completed all 3 instruments, and responses were analyzed using calculated Pearson's r and Cronbach's α. The authors found that the ILS active-reflective and sensing-intuitive scores as well as the LSI active-reflective scores were valid in determining learning styles. However, the ILS sequential-global domain failed to correlate well with other instruments and may be flawed, at least in this given population. The authors advised the use of caution when interpreting scores without a strong knowledge of construct definitions and empirical evidence.

Several other instruments designed to measure personality indexes or psychological types may overlap and describe learning styles in nonspecific fashions. One example of such an indicator is the Myers-Briggs Index. 6 While some relation between personality indexes and learning styles may exist, the use of instruments intended to describe personality to characterize learning style has been criticized by several authors. Therefore, the use of these markers to measure learning styles is not recommended. 6 The concept of emotional intelligence is another popular way to characterize intellect and learning capacity but similarly should not be misconstrued as an effective means of describing learning styles. 23

Several authors have proposed correlations between culture and learning styles. 6 , 24 This is predicated on the concept that culture influences environmental perceptions which, in turn, to some degree determine the way in which information is processed and organized. The storage, processing, and assimilation methods for information contribute to how new knowledge is learned. Culture also plays a role in conditioning and reinforcing learning styles and partially explains why teaching methods used in certain parts of the world may be ineffective or less effective when blindly transplanted to another locale. 6 , 24 Teachers should be aware of this phenomenon and the influence it has on the variety of learning styles that are present in classrooms. This is especially true in classrooms that have a large contingency of international students. Such classrooms are becoming increasingly common as more and more schools expand their internationalization efforts. 25

The technological age may also be influencing the learning styles of younger students and emerging generations of learners. The Millennial Generation has been described as more technologically advanced than their Generation X counterparts, with higher expectations for the use of computer-aided media in the classroom. 15 , 16 , 26 Younger students are accustomed to enhanced visual images associated with various computer- and television-based games and game systems. 16 , 26 Additionally, video technology is increasingly becoming “transportable” in the way of mobile computing, MP3 devices, personal digital video players, and other technologies. 26 All of these advances have made visual images more pervasive and common within industrialized nations.


As class sizes increase, so do the types and numbers of student learning styles. Also, as previously mentioned, internationalization and changes in the media culture may affect the spectrum of classroom learning styles as well. 24 , 25 Given the variability in learning styles that may exist in a classroom, some authors suggested that students should adapt their learning styles to coincide with a given instruction style. 6 , 27 This allows instructors to dictate the methods used to instruct in the classroom. This approach also allows instructors to “teach from their strengths,” with little consideration to other external factors such as learning style of students. While convenient, this unilateral approach has been criticized for placing all of the responsibility for aligning teaching and learning on the student. When the majority of information is presented in formats that are misaligned with learning styles, students may spend more time manipulating material than they do in comprehending and applying the information. Additionally, a unilaterally designed classroom may reinforce a “do nothing” approach among faculty members. 6 Alternatively, a teaching style-learning style mismatch might challenge students to adjust, grow intellectually, and learn in more integrated ways. However, it may be difficult to predict which students have the baseline capacity to adjust, particularly when significant gaps in knowledge of a given subject already exist or when the learner is a novice to the topic being instructed. 6 , 27 This might be especially challenging within professional curricula where course load expectations are significant.

Best practice most likely involves a teaching paradigm which addresses and accommodates multiple dimensions of learning styles that build self-efficacy. 27 Instructing in a way that encompasses multiple learning styles gives the teacher an opportunity to reach a greater extent of a given class, while also challenging students to expand their range of learning styles and aptitudes at a slower pace. This may avoid lost learning opportunities and circumvent unnecessary frustration from both the teacher and student. For many instructors, multi-style teaching is their inherent approach to learning, while other instructors more commonly employ unilateral styles. Learning might be better facilitated if instructors were cognizant of both their teaching styles and the learning styles of their students. An understanding and appreciation of a given individual's teaching style requires self-reflection and introspection and should be a component of a well-maintained teaching portfolio. Major changes or modifications to teaching styles might not be necessary in order to effectively create a classroom atmosphere that addresses multiple learning styles or targets individual ones. However, faculty members should be cautious to not over ambitiously, arbitrarily, or frivolously design courses and activities with an array of teaching modalities that are not carefully connected, orchestrated, and delivered.

Novice learners will likely be more successful when classrooms, either by design or by chance, are tailored to their learning style. However, the ultimate goal is to instill within students the skills to recognize and react to various styles so that learning is maximized no matter what the environment. 28 This is an essential skill for an independent learner and for students in any career path.

Particular consideration of learning styles might be given to asynchronous courses and other courses where a significant portion of time is spent online. 29 As technology advances and classroom sizes in many institutions become increasingly large, asynchronous instruction is becoming more pervasive. In many instances, students who have grown accustomed to technological advances may prefer asynchronous courses. Online platforms may inherently affect learning on a single dimension (visual or auditory). Most researchers who have compared the learning styles of students enrolled in online versus traditional courses have found no correlations between the learning styles and learning outcomes of cohorts enrolled in either course type. Johnson et al compared learning style profiles to student satisfaction with either online or face-to-face study groups. 30 Forty-eight college students participated in the analysis. Learning styles were measured using the ILS. Students were surveyed with regard to their satisfaction with various study group formats. These results were then correlated to actual performance on course examinations. Active and visual learners demonstrated a significant preference for face-to-face study groups. Alternatively, students who were reflective learners demonstrated a preference for online groups. Likely due to the small sample size, none of these differences achieved statistical significance. The authors suggest that these results are evidence for courses employing hybrid teaching styles that reach as many different students as possible. Cook et al studied 121 internal medicine residents and also found no association (p > 0.05) between ILS-measured learning styles and preferences for learning formats (eg, Web-based versus paper-based learning modules). 31 Scores on assessment questions related to learning modules administered to the residents were also not statistically correlated with learning styles.

Cook et al examined the effectiveness of adapting Web-based learning modules to a given learner's style. 32 The investigators created 2 versions of a Web-based instructional module on complementary and alternative medications. One version of the modules directed the learner to “active” questions that provided learners immediate and comprehensive feedback, while the other version involved “reflective” questions that directed learners back to the case content for answers. Eighty-nine residents were randomly matched or mismatched based on their active-reflective learning styles (as determined by ILS) to either the “active” or “reflective” test version. Posttest scores for either question type among mismatched subjects did not differ significantly ( p = 0.97), suggesting no interaction between learning styles and question types. The authors concluded from this small study that learning styles had no influence on learning outcomes. The study was limited in its lack of assessment of baseline knowledge, motivation, or other characteristics. Also, the difficulty of the assessment may not have been sufficient enough to distinguish a difference and/or “mismatched” learners may have automatically adapted to the information they received regardless of type.


There are no published studies that have systematically examined the learning styles of pharmacy students. Pungente et al collected some learning styles data as part of a study designed to evaluate how first-year pharmacy students' learning styles influenced preferences toward different activities associated with problem-based learning (PBL). 33 One hundred sixteen first-year students completed Kolb's LSI. Learning styles were then matched to responses from a survey designed to assess student preferences towards various aspects of PBL. The majority of students were classified by the LSI as being accommodators (36.2%), with a fairly even distribution of styles among remaining students (19.8% assimilators, 22.4% convergers, 21.6% divergers). There was a proportional distribution of learning styles among a convenience sample of pharmacy students. Divergers were the least satisfied with the PBL method of instruction, while convergers demonstrated the strongest preference for this method of learning. The investigators proposed that the next step might be to correlate learning styles and PBL preferences with actual academic success.

Limited research correlating learning styles to learning outcomes has hampered the application of learning style theory to actual classroom settings. Complicating research is the plethora of different learning style measurement instruments available. Despite these obstacles, efforts to better define and utilize learning style theory is an area of growing research. A better knowledge and understanding of learning styles may become increasingly critical as classroom sizes increase and as technological advances continue to mold the types of students entering higher education. While research in this area continues to grow, faculty members should make concentrated efforts to teach in a multi-style fashion that both reaches the greatest extent of students in a given class and challenges all students to grow as learners.

  • Reference Manager
  • Simple TEXT file

People also looked at

Brief research report article, the learning styles educational neuromyth: lack of agreement between teachers' judgments, self-assessment, and students' intelligence.

learning styles research articles

  • 1 Department of Primary Education, School of Education, National and Kapodistrian University of Athens, Athens, Greece
  • 2 School of Education and Social Work, University of Dundee, Scotland, United Kingdom

Learning styles (LS) have dominated educational practice since their popularization in the 1970s. Studies have shown that they are accepted by more than 90% of teachers worldwide. However, LS have also received extensive criticism from researchers and academics, due to the poor theoretical justification of the theory, their problematic measurement, and the lack of systematic studies supporting them. The present study tested the hypothesis that teachers' and students' assessment of preferred LS should correspond. Moreover, it tested whether teachers' judgment of LS is driven by the students' IQ. Both questions were studied for the first time in a systematic fashion within LS research in primary school pupils. Fifth and sixth grade pupils ( n = 199) were asked to self-assess their preferred LS, while their teachers were asked to provide their own assessment on individual pupils' LS. No relationship was found between pupils' self-assessment and teachers' assessment, suggesting that teachers cannot assess the LS of their students accurately. Moreover, students' intelligence was not found to drive teachers' assessment of their LS. This study adds to the body of evidence that is skeptical of the adoption of LS in mainstream education.


The term Learning Styles (LS) is used to describe the idea that different individuals differ in the modality of instruction that is most effective to them ( Pashler et al., 2008 ). Criticism of the concept of LS has been widespread ( Curry, 1990 ; Coffield et al., 2004 ; Geake, 2008 ) and in 2002 the Organization for Economic Cooperation and Development (OECD), through its Centre for Educational Research and Innovation (CERI), pronounced LS a neuromyth ( OECD, 2002 ). The OECD classification was particularly concerned with the three LS that are often seen in educational practice, namely the visual, auditory, or haptic (kinaesthetic) types ( OECD, 2002 ).

Despite the lack of evidence in support of the concept, LS remain ever popular with a great majority of educators. A study looking at teachers from the UK and the Netherlands showed that more than 90% of teachers believe there is an optimal delivery style for each learner ( Dekker et al., 2012 ). Similar studies have found equally high numbers in Spain ( Ferrero et al., 2016 ) and Portugal ( Rato et al., 2013 ). In Greece, the setting for this current study, 97% of practicing teachers believe that students' performance can be enhanced when material is delivered in an individual's preferred LS ( Deligiannidi and Howard-Jones, 2015 ) and 94% of student teachers agree ( Papadatou-Pastou et al., 2017 ).

Only a few empirical studies have sought to shed light on the rather obscure picture ( Marcus, 1977 ; Rogowsky et al., 2015 ). For example, Rogowsky et al. (2015) investigated the effect of LS preference in text comprehension in an adult sample. According to the findings, no statistical significance was to be found in the relationship between LS preference, mode of delivery, and learning aptitude.

Building upon this evidence, the current study was designed. Its main aim was to assess whether the LS of primary school aged pupils as assessed by the students and their teachers, would agree. These are important questions, as teachers typically adopt LS within a classroom context by relying on their own assessment of students LS ( Cassidy, 2004 ; Graf and Liu, 2009 ). Moreover, there has been limited research done on primary-aged pupils (e.g., Sun et al., 2008 ), with research mainly available on older students or adult samples ( Diaz and Cartnal, 1999 ; Massa and Mayer, 2006 ; Husmann and O'Loughlin, 2018 ). There is also very limited literature relating LS to IQ ( Dunn and Price, 1980 ; Barbe and Milone, 1982 ; Griggs and Dunn, 1984 ; Dunn, 1990 ) and no studies investigating the hypothesis of whether teachers confuse their students' intellectual ability with a specific LS. However, there is previous research to suggest that teachers can erroneously associate IQ with other characteristics, such as being left-or right-handed ( Papadatou-Pastou et al., 2017 ), or socio-economic status and gender ( Auwarter and Aruguete, 2008 ).

In the current study, intellectual ability was measured by means of a fluid intelligence test, Raven's Colored Progressive Matrices (CPM; Raven et al., 2004 ), which is “the paradigm test of non-verbal, abstract reasoning ability” ( Mackintosh, 1996 , p. 564) and is widely regarded as one of the best tests of Spearman's g, the general factor underlying all cognitive abilities ( Spearman, 1946 ). Raven's matrices have been frequently used in educational research ( Brouwers et al., 2009 ), and have been shown to have good construct validity across age, gender, and country ( Rushton et al., 2003 , 2004 ). Across cultures with a tradition of literacy, like the Greek culture, the norms for the RPM have been shown to be unexpectedly similar ( Raven, 2008 ). Moreover, in contrast to full-scale IQ tests, such as the Wechsler Intelligence Scale for Children (WISC; Kaufman et al., 2015 ), it is easy to administer and can be completed through group administration.

To conclude, the main aim of the study was to investigate whether there is an association between primary school pupils' self-report of their preferred LS and teachers' evaluation of each pupil's LS. The second aim of the study was to investigate whether teachers' assessments of LS would be informed by their students' intellectual ability.


One hundred and ninety nine 5th and 6th grade primary school students including 105 girls (mean age = 135.90 months, SD = 7.27, range = 125–149) and 94 boys (mean age = 136.25 months, SD = 7.28, range = 121–148) participated in this study after their parents gave written informed consent. Five state schools in the first district of the municipality of Athens were recruited to take part in the study. Nineteen teachers (15 women; mean age = 50.52 years, SD = 5.24, range = 31–55) also participated after giving written informed consent. Their mean teaching experience was 20.05 years (SD = 4.05, range = 7–25). Ten teachers taught 5th grade and nine 6th grade level. The study was granted ethical approval by the Institute of Educational Policy, supervised by the Greek Ministry of Education, Research, and Religious Affairs (protocol number: Φ15/1181/174596/Δ1). Written informed consent was given in accordance with the Declaration of Helsinki.


Forced-choice LS question . Student participants were asked whether they are auditory, visual, or kinaesthetic learners. Students had to circle among three choices “visual,” “auditory,” “kinaesthetic.”

Raven's Colored Progressive Matrices ( Raven et al., 2004 ) . The CPM is a measure of fluid intelligent and is considered to be a culture-fair intelligence test ( Van de Vijver and Hambleton, 1996 ). It comprises three sets of 12 items. For each item students have to identify a missing piece in a pattern, choosing among six possible options. Each set of items gets progressively harder. Raw scores were matched with each participant's chronological age in order to calculate IQ scores.

Teachers Questionnaire

Teachers were asked to respond to two items, namely “Does teaching that is tailor made to the students' LS reinforce the students' performance?,” which was an open-ended question, and “Which is the learning style of each of your students?” with possible responses to the latter question being auditory, visual, or kinaesthetic. Each teacher provided only one LS for each student, after being prompted to recall specific incidents from the classroom. Each student's LS was judged by one teacher.

Statistical Analysis

All analyses were performed using the Statistical Package for the Social Sciences (SPSS) v.25. In order to analyze the qualitative data collected from the open-ended question, word cloud analysis was used, which graphically represents word frequency, giving greater prominence to words that appear more frequently in the participants' responses. In addition, the most characteristic segments of text were presented. The analysis was performed using Iramuteq ( Ratinaud and Dejean, 2009 ), an R interface for multidimensional analysis of texts and questionnaires. Word clouds are increasingly being employed in exploratory qualitative analysis in order to identify the focus of written material ( Atenstaedt, 2012 ). In order to investigate a possible association between the two types of LS assessment (student's self-assessment and teachers' assessment), χ 2 analysis was performed. In order to test whether the three LS, as assessed by the teachers, differed in terms of IQ, analysis of variance (ANOVA) was performed, with the biological sex of the students and the LS of the students according to the teachers as the grouping factors. Whether teachers adopted the LS styles myth could not be used, as all teachers reported that they believe in LS. The partial eta squared (η 2 ) statistic was used as the effect size measure. All p -values were two-tailed and the α-level was set at 0.05. All data and quantitative analysis code are available in the Open Science Framework repository ( https://osf.io/a9g7s/ ).

All teachers reported that they believed that teaching tailored to the students' LS enhances the intake of new information. However, only four teachers referred to the VAK explicitly, that is by using the words visual, auditory, and/or kinaesthetic. For example, one female teacher reported, “ Yes, of course I try to support the students whom I have found out to be visual, auditory, or kinaesthetic types with material that I design myself or that I find online.” Most teachers, however, referred to “learning styles” in a more general fashion or did not make it clear in their responses they referred to the VAK model. For example a male teacher reported “ Students' performance is enhanced when using material that I create personally through handicrafts or through a computer.” and a female teacher reported “ Yes, teaching is tailored to the learning styles of the students sometimes and there is a great enhancement in their performance.”

Figure 1 presents the word cloud stemming from the text of the teachers' responses to the open-ended question, after removing common words, such as “and.” The words that were more prominent, as indicated by the size of the words in the word cloud were “students,” “performance,” “learning,” “ teaching,” and “material.”


Figure 1 . Word cloud representing the most frequent words, giving greater prominence to words that appear more frequently, in the teachers' responses to the open-ended question: “Does teaching that is tailor made to the students' learning styles reinforce the students' performance?”.

Table 1 presents the LS of the students as assessed by self-assessment and by the teachers. A χ 2 analysis was performed to test for their possible association. No statistically significant associations were found to exist; self-assessment—teacher-assessment, χ ( 4 ) 2 = 4.86, p = 0.30.


Table 1 . Crosstabulation of Learning Styles (LS) as measured by self-assessment by the students and by the assessment of teachers.

A 2 × 3 (ANOVA) was performed, with the biological sex of the students (male or female) and the LS of the students according to the teachers (visual, auditory, or kinaesthetic) as the grouping factors and the IQ score as the dependent variable. No main effect of LS was found, F (2,198) = 0.38, p = 0.69, η 2 = 0.004 (mean raw Raven score for visual types = 30.86, SD = 3.60, mean raw Raven score for auditory types = 30.27, SD = 4.45, mean raw Raven score for kinaesthetic types = 30.16, SD = 4.73) or main effect of sex, F (1,198) = 0.21, p = 0.65, η 2 = 0.001 (mean raw Raven score for males = 30.29, SD = 4.51, mean raw Raven score for females = 30.44, SD = 4.31). No interaction was found between LS and sex, F (2,198) = 1.20, p = 0.30, η 2 = 0.012.

The present study looked at whether self-assessment and teacher assessment agreed in the identification of preferred LS in primary school-aged pupils. Results show that there is no correlation between the two. Findings, moreover, suggest that the teachers do not see intellectual ability as a proxy for a particular learning style. This was the first study to investigate these questions and one of the few studies within the LS literature to employ a sample of primary school students. It adds to the growing body of critical literature about the use of LS in educational settings ( Coffield et al., 2004 ; Franklin, 2006 ; Pashler et al., 2008 ).

The present study focused at a certain type of LS, VAK, as it is very commonly used in primary schools, with each student's LS assessed by one teacher ( Sharp et al., 2008 ). Teachers were asked in an open-ended question whether they believed that teaching tailored to the students' LS enhances the intake of new information. The phrasing of this question did not refer specifically to the VAK typology, and this was also reflected in the teachers' responses, as only four teachers referred to the VAK model per se . The rest of the teachers referred to “learning types” in a more general manner, possibly reflecting the vague nature of the concept. This was a limitation of the present study, as we could not ascertain if the teachers adopted the VAK model specifically and could further not test for possible differences between those teachers who adopt the model and those who do not. Future studies should add a question on the VAK typology, as it could be the case that teachers believe in LS, but not specifically to the VAK model. Moreover, judgments made by different teachers for the same students could be compared.

Overall, we posit that identifying preferred LS can be a hit-and-miss process, with no agreement between the assessment made by teachers and students. We suggest that if the identification of LS, as they are currently understood and used within primary education, is unreliable, as evident by the findings of the present study, this should constitute an additional reason why teachers should abandon the use of LS in instruction. Our study thus adds to the growing body of literature against the use of LS in education. Moreover, debunking the myth of LS as well as educating teachers in the use of evidence-based practices is recommended.

Ethics Statement

All subjects gave written informed consent in accordance with the Declaration of Helsinki. The study was granted ethical approval by the Institute of Educational Policy, supervised by the Greek Ministry of Education, Research and Religious Affairs.

Author Contributions

MP-P conceived and designed the study. AB was consulted at the initial stages. MG collected the data. MP-P analyzed the data. AB and MP-P did the drafting, and revising of the work, and wrote the final manuscript. MP-P supervised the project from conception to submission.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Atenstaedt, R. (2012). Word cloud analysis of the BJGP. Br. J. Gen. Pract. 62:148. doi: 10.3399/bjgp12X630142

PubMed Abstract | CrossRef Full Text | Google Scholar

Auwarter, A. E., and Aruguete, M. S. (2008). Effects of student gender and socioeconomic status on teacher perceptions. J. Educ. Res. 101, 242–246. doi: 10.3200/JOER.101.4.243-246

CrossRef Full Text | Google Scholar

Barbe, W. B., and Milone, M. N. Jr. (1982). Modality characteristics of gifted children. Gifted Child Today 5, 2–5.

Google Scholar

Brouwers, S. A., Van de Vijver, F. J., and Van Hemert, D. A. (2009). Variation in Raven's progressive matrices scores across time and place. Learn. Individ. Differ. 19, 330–338. doi: 10.1016/j.lindif.2008.10.006

Cassidy, S. (2004). Learning styles: an overview of theories, models, and measures. Educ. Psychol. 24, 419–444. doi: 10.1080/0144341042000228834

Coffield, F., Moseley, D., Hall, E., and Ecclestone, K. (2004). Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review. London: Learning and Skills Research Centre.

Curry, L. (1990). A critique of the research on learning styles. Educ. Leader. 48, 50–56.

Dekker, S., Lee, N. C., Howard-Jones, P., and Jolles, J. (2012). Neuromyths in education: prevalence and predictors of misconceptions among teachers. Front. Psychol. 3:429. doi: 10.3389/fpsyg.2012.00429

Deligiannidi, K., and Howard-Jones, P. (2015). The neuroscience literacy of teachers in Greece. Proc. Soc. Behav. Sci. 174, 3909–3915. doi: 10.1016/j.sbspro.2015.01.1133

Diaz, D. P., and Cartnal, R. B. (1999). Students' learning styles in two classes: online distance learning and equivalent on-campus. Coll. Teach. 47, 130–135. doi: 10.1080/87567559909595802

Dunn, R. (1990). Understanding the Dunn and Dunn learning styles model and the need for individual diagnosis and prescription. Read. Writ. Q. 6, 223–247. doi: 10.1080/0748763900060303

Dunn, R. S., and Price, G. E. (1980). The learning style characteristics of gifted students. Gifted Child Q. 24, 33–36. doi: 10.1177/001698628002400107

Ferrero, M., Garaizar, P., and Vadillo, M. A. (2016). Neuromyths in education: prevalence among Spanish teachers and an exploration of cross-cultural variation. Front. Hum. Neurosci. 10:496. doi: 10.3389/fnhum.2016.00496

Franklin, S. (2006). VAKing out learning style why the notion of ‘learning styles' is unhelpful to teachers. Education 34, 81–87. doi: 10.1080/03004270500507644

Geake, J. (2008). Neuromythologies in education. Educ. Res. 50, 123–133. doi: 10.1080/00131880802082518

Graf, S., and Liu, T.-C. (2009). Supporting teachers in identifying students' learning styles in learning management systems: an automatic student modelling approach. J. Educ. Technol. Soc. 12, 3–14.

Griggs, S. A., and Dunn, R. S. (1984). Selected case studies of the learning style preferences of gifted students. Gifted Child Q. 28, 115–119. doi: 10.1177/001698628402800304

Husmann, P. R., and O'Loughlin, V. D. (2018). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students' study strategies, class performance, and reported VARK learning styles. Anat. Sci. Educ. doi: 10.1002/ase.1777. [Epub ahead of print].

Kaufman, A. S., Raiford, S. E., and Coalson, D. L. (2015). Intelligent Testing With the WISC-V . John Wiley & Sons.

Mackintosh, N. J. (1996). Sex differences and IQ. J. Biosoc. Sci. 28, 559–572. doi: 10.1017/S0021932000022586

Marcus, L. (1977). How teachers view student learning styles. NASSP Bull. 61, 112–114. doi: 10.1177/019263657706140821

Massa, L. J., and Mayer, R. E. (2006). Testing the ATI hypothesis: should multimedia instruction accommodate verbalizer-visualizer cognitive style? Learn. Individ. Differ. 16, 321–335. doi: 10.1016/j.lindif.2006.10.001

OECD (2002). Organisation for Economic Co-operation and Development. Understanding The Brain: Towards a New Learning Science. Paris: OECD Publishing.

Papadatou-Pastou, M., Haliou, E., and Vlachos, F. (2017). Brain knowledge and the prevalence of neuromyths among prospective teachers in Greece. Front. Psychol. 8:804. doi: 10.3389/fpsyg.2017.00804

Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. (2008). Learning styles: concepts and evidence. Psychol. Sci. Public Interest 9, 105–119. doi: 10.1111/Fj.1539-6053.2009.01038.x

Ratinaud, P., and Dejean, S. (2009). IRaMuTeQ: Implémentation de la Méthode ALCESTE d'Analyse de Texte Dans un Logiciel Libre . Toulouse: Modélisation Appliquée aux Sciences Humaines et Sociales (MASHS2009)

Rato, J. R., Abreu, A. M., and Castro-Caldas, A. (2013). Neuromyths in education: what is fact and what is fiction for Portuguese teachers? Educ. Res. 55, 441–453. doi: 10.1080/00131881.2013.844947

Raven, J. (2008). The Raven progressive matrices tests: their theoretical basis and measurement model in Uses and Abuses of Intelligence. Studies Advancing Spearman and Raven's Quest for Non-arbitrary Metrics (Raven), 17–68.

Raven, J., Raven, J. C., and Court, J. H. (2004). Manual for Raven's Progressive Matrices and Vocabulary Scales. Section 3: Standard Progressives Matrices: 1998 Edition . San Antonio, TX: Harcourt Assessment.

Rogowsky, B. A., Calhoun, B. M., and Tallal, P. (2015). Matching learning style to instructional method: effects on comprehension. J. Educ. Psychol. 107, 64–78. doi: 10.1037/a0037478

Rushton, J. P., Skuy, M., and Bons, T. A. (2004). Construct validity of Raven's advanced progressive matrices for African and non-African engineering students in South Africa. Int. J. Select. Assess. 12, 220–229. doi: 10.1111/j.0965-075X.2004.00276.x

Rushton, J. P., Skuy, M., and Fridjhon, P. (2003). Performance on Raven's advanced progressive matrices by African, East Indian, and white engineering students in South Africa. Intelligence 31, 123–137. doi: 10.1016/S0160-2896(02)00140-X

Sharp, J. G., Byrne, J., and Bowker, R. (2008). The trouble with VAK. Educ. Fut. 1, 89–97.

Spearman, C. (1946). Theory of general factor. Br. J. Psychol. 36, 117–131.

Sun, K. T., Lin, Y. C., and Yu, C. J. (2008). A study on learning effect among different learning styles in a web-based lab of science for elementary school students. Comput. Educ. 50, 1411–1422. doi: 10.1016/j.compedu.2007.01.003

Van de Vijver, F., and Hambleton, R. K. (1996). Translating tests. Eur. Psychol. 1, 89–99. doi: 10.1027/1016-9040.1.2.89

Keywords: learning styles, auditory, visual, kinaesthetic, neuromyths, VAK, intelligence

Citation: Papadatou-Pastou M, Gritzali M and Barrable A (2018) The Learning Styles Educational Neuromyth: Lack of Agreement Between Teachers' Judgments, Self-Assessment, and Students' Intelligence. Front. Educ . 3:105. doi: 10.3389/feduc.2018.00105

Received: 26 September 2018; Accepted: 14 November 2018; Published: 29 November 2018.

Reviewed by:

Copyright © 2018 Papadatou-Pastou, Gritzali and Barrable. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marietta Papadatou-Pastou, [email protected]

  • Research article
  • Open access
  • Published: 01 October 2021

Adaptive e-learning environment based on learning styles and its impact on development students' engagement

  • Hassan A. El-Sabagh   ORCID: orcid.org/0000-0001-5463-5982 1 , 2  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  53 ( 2021 ) Cite this article

51k Accesses

46 Citations

27 Altmetric

Metrics details

Adaptive e-learning is viewed as stimulation to support learning and improve student engagement, so designing appropriate adaptive e-learning environments contributes to personalizing instruction to reinforce learning outcomes. The purpose of this paper is to design an adaptive e-learning environment based on students' learning styles and study the impact of the adaptive e-learning environment on students’ engagement. This research attempts as well to outline and compare the proposed adaptive e-learning environment with a conventional e-learning approach. The paper is based on mixed research methods that were used to study the impact as follows: Development method is used in designing the adaptive e-learning environment, a quasi-experimental research design for conducting the research experiment. The student engagement scale is used to measure the following affective and behavioral factors of engagement (skills, participation/interaction, performance, emotional). The results revealed that the experimental group is statistically significantly higher than those in the control group. These experimental results imply the potential of an adaptive e-learning environment to engage students towards learning. Several practical recommendations forward from this paper: how to design a base for adaptive e-learning based on the learning styles and their implementation; how to increase the impact of adaptive e-learning in education; how to raise cost efficiency of education. The proposed adaptive e-learning approach and the results can help e-learning institutes in designing and developing more customized and adaptive e-learning environments to reinforce student engagement.


In recent years, educational technology has advanced at a rapid rate. Once learning experiences are customized, e-learning content becomes richer and more diverse (El-Sabagh & Hamed, 2020 ; Yang et al., 2013 ). E-learning produces constructive learning outcomes, as it allows students to actively participate in learning at anytime and anyplace (Chen et al., 2010 ; Lee et al., 2019 ). Recently, adaptive e-learning has become an approach that is widely implemented by higher education institutions. The adaptive e-learning environment (ALE) is an emerging research field that deals with the development approach to fulfill students' learning styles by adapting the learning environment within the learning management system "LMS" to change the concept of delivering e-content. Adaptive e-learning is a learning process in which the content is taught or adapted based on the responses of the students' learning styles or preferences. (Normadhi et al., 2019 ; Oxman & Wong, 2014 ). By offering customized content, adaptive e-learning environments improve the quality of online learning. The customized environment should be adaptable based on the needs and learning styles of each student in the same course. (Franzoni & Assar, 2009 ; Kolekar et al., 2017 ). Adaptive e-learning changes the level of instruction dynamically based on student learning styles and personalizes instruction to enhance or accelerate a student's success. Directing instruction to each student's strengths and content needs can minimize course dropout rates, increase student outcomes and the speed at which they are accomplished. The personalized learning approach focuses on providing an effective, customized, and efficient path of learning so that every student can participate in the learning process (Hussein & Al-Chalabi, 2020 ). Learning styles, on the other hand, represent an important issue in learning in the twenty-first century, with students expected to participate actively in developing self-understanding as well as their environment engagement. (Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Truong, 2016 ).

In current conventional e-learning environments, instruction has traditionally followed a “one style fits all” approach, which means that all students are exposed to the same learning procedures. This type of learning does not take into account the different learning styles and preferences of students. Currently, the development of e-learning systems has accommodated and supported personalized learning, in which instruction is fitted to a students’ individual needs and learning styles (Beldagli & Adiguzel, 2010 ; Benhamdi et al., 2017 ; Pashler et al., 2008 ). Some personalized approaches let students choose content that matches their personality (Hussein & Al-Chalabi, 2020 ). The delivery of course materials is an important issue of personalized learning. Moreover, designing a well-designed, effective, adaptive e-learning system represents a challenge due to complication of adapting to the different needs of learners (Alshammari, 2016 ). Regardless of using e-learning claims that shifting to adaptive e-learning environments to be able to reinforce students' engagement. However, a learning environment cannot be considered adaptive if it is not flexible enough to accommodate students' learning styles. (Ennouamani & Mahani, 2017 ).

On the other hand, while student engagement has become a central issue in learning, it is also an indicator of educational quality and whether active learning occurs in classes. (Lee et al., 2019 ; Nkomo et al., 2021 ; Robinson & Hullinger, 2008 ). Veiga et al. ( 2014 ) suggest that there is a need for further research in engagement because assessing students’ engagement is a predictor of learning and academic progress. It is important to clarify the distinction between causal factors such as learning environment and outcome factors such as achievement. Accordingly, student engagement is an important research topic because it affects a student's final grade, and course dropout rate (Staikopoulos et al., 2015 ).

The Umm Al-Qura University strategic plan through common first-year deanship has focused on best practices that increase students' higher-order skills. These skills include communication skills, problem-solving skills, research skills, and creative thinking skills. Although the UQU action plan involves improving these skills through common first-year academic programs, the student's learning skills need to be encouraged and engaged more (Umm Al-Qura University Agency, 2020 ). As a result of the author's experience, The conventional methods of instruction in the "learning skills" course were observed, in which the content is presented to all students in one style that is dependent on understanding the content regardless of the diversity of their learning styles.

According to some studies (Alshammari & Qtaish, 2019 ; Lee & Kim, 2012 ; Shih et al., 2008 ; Verdú, et al., 2008 ; Yalcinalp & Avc, 2019 ), there is little attention paid to the needs and preferences of individual learners, and as a result, all learners are treated in the same way. More research into the impact of educational technologies on developing skills and performance among different learners is recommended. This “one-style-fits-all” approach implies that all learners are expected to use the same learning style as prescribed by the e-learning environment. Subsequently, a review of the literature revealed that an adaptive e-learning environment can affect learning outcomes to fill the identified gap. In conclusion: Adaptive e-learning environments rely on the learner's preferences and learning style as a reference that supports to create adaptation.

To confirm the above: the author conducted an exploratory study via an open interview that included some questions with a sample of 50 students in the learning skills department of common first-year. Questions asked about the difficulties they face when learning a "learning skills" course, what is the preferred way of course content. Students (88%) agreed that the way students are presented does not differ according to their differences and that they suffer from a lack of personal learning that is compatible with their style of work. Students (82%) agreed that they lack adaptive educational content that helps them to be engaged in the learning process. Accordingly, the author handled the research problem.

This research supplements to the existing body of knowledge on the subject. It is considered significant because it improves understanding challenges involved in designing the adaptive environments based on learning styles parameter. Subsequently, this paper is structured as follows: The next section presents the related work cited in the literature, followed by research methodology, then data collection, results, discussion, and finally, some conclusions and future trends are discussed.

Theoretical framework

This section briefly provides a thorough review of the literature about the adaptive E-learning environments based on learning styles.

Adaptive e-learning environments based on learning styles

The adaptive e-learning employment in higher education has been slower to evolve, and challenges that led to the slow implementation still exist. The learning management system offers the same tools to all learners, although individual learners need different details based on learning style and preferences. (Beldagli & Adiguzel, 2010 ; Kolekar et al., 2017 ). The interactive e-learning environment requisite evaluating the learner's desired learning style, before the course delivery, such as an online quiz or during the course delivery, such as tracking student reactions (DeCapua & Marshall, 2015 ).

In e-learning environments, adaptation is constructed on a series of well-designed processes to fit the instructional materials. The adaptive e-learning framework attempt to match instructional content to the learners' needs and styles. According to Qazdar et al. ( 2015 ), adaptive e-learning (AEL) environments rely on constructing a model of each learner's needs, preferences, and styles. It is well recognized that such adaptive behavior can increase learners' development and performance, thus enriching learning experience quality. (Shi et al., 2013 ). The following features of adaptive e-learning environments can be identified through diversity, interactivity, adaptability, feedback, performance, and predictability. Although adaptive framework taxonomy and characteristics related to various elements, adaptive learning includes at least three elements: a model of the structure of the content to be learned with detailed learning outcomes (a content model). The student's expertise based on success, as well as a method of interpreting student strengths (a learner model), and a method of matching the instructional materials and how it is delivered in a customized way (an instructional model) (Ali et al., 2019 ). The number of adaptive e-learning studies has increased over the last few years. Adaptive e-learning is likely to increase at an accelerating pace at all levels of instruction (Hussein & Al-Chalabi, 2020 ; Oxman & Wong, 2014 ).

Many studies assured the power of adaptive e-learning in delivering e-content for learners in a way that fitting their needs, and learning styles, which helps improve the process of students' acquisition of knowledge, experiences and develop their higher thinking skills (Ali et al., 2019 ; Behaz & Djoudi, 2012 ; Chun-Hui et al., 2017 ; Daines et al., 2016 ; Dominic et al., 2015 ; Mahnane et al., 2013 ; Vassileva, 2012 ). Student characteristics of learning style are recognized as an important issue and a vital influence in learning and are frequently used as a foundation to generate personalized learning experiences (Alshammari & Qtaish, 2019 ; El-Sabagh & Hamed, 2020 ; Hussein & Al-Chalabi, 2020 ; Klasnja-Milicevic et al., 2011 ; Normadhi et al., 2019 ; Ozyurt & Ozyurt, 2015 ).

The learning style is a parameter of designing adaptive e-learning environments. Individuals differ in their learning styles when interacting with the content presented to them, as many studies emphasized the relationship between e-learning and learning styles to be motivated in learning situations, consequently improving the learning outcomes (Ali et al., 2019 ; Alshammari, 2016 ; Alzain et al., 2018a , b ; Liang, 2012 ; Mahnane et al., 2013 ; Nainie et al., 2010 ; Velázquez & Assar, 2009 ). The word "learning style" refers to the process by which the learner organizes, processes, represents, and combines this information and stores it in his cognitive source, then retrieves the information and experiences in the style that reflects his technique of communicating them. (Fleming & Baume, 2006 ; Jaleel & Thomas, 2019 ; Jonassen & Grabowski, 2012 ; Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Pashler et al., 2008 ; Willingham et al., 2105 ; Zhang, 2017 ). The concept of learning style is founded based on the fact that students vary in their styles of receiving knowledge and thought, to help them recognizing and combining information in their mind, as well as acquire experiences and skills. (Naqeeb, 2011 ). The extensive scholarly literature on learning styles is distributed with few strong experimental findings (Truong, 2016 ), and a few findings on the effect of adapting instruction to learning style. There are many models of learning styles (Aldosarim et al., 2018 ; Alzain et al., 2018a , 2018b ; Cletus & Eneluwe, 2020 ; Franzoni & Assar, 2009 ; Willingham et al., 2015 ), including the VARK model, which is one of the most well-known models used to classify learning styles. The VARK questionnaire offers better thought about information processing preferences (Johnson, 2009 ). Fleming and Baume ( 2006 ) developed the VARK model, which consists of four students' preferred learning types. The letter "V" represents for visual and means the visual style, while the letter "A" represents for auditory and means the auditory style, and the letter "R/W" represents "write/read", means the reading/writing style, and the letter "K" represents the word "Kinesthetic" and means the practical style. Moreover, VARK distinguishes the visual category further into graphical and textual or visual and read/write learners (Murphy et al., 2004 ; Leung, et al., 2014 ; Willingham et al., 2015 ). The four categories of The VARK Learning Style Inventory are shown in the Fig. 1 below.

figure 1

VARK learning styles

According to the VARK model, learners are classified into four groups representing basic learning styles based on their responses which have 16 questions, there are four potential responses to each question, where each answer agrees to one of the extremes of the dimension (Hussain, 2017 ; Silva, 2020 ; Zhang, 2017 ) to support instructors who use it to create effective courses for students. Visual learners prefer to take instructional materials and send assignments using tools such as maps, graphs, images, and other symbols, according to Fleming and Baume ( 2006 ). Learners who can read–write prefer to use written textual learning materials, they use glossaries, handouts, textbooks, and lecture notes. Aural learners, on the other hand, prefer to learn through spoken materials, dialogue, lectures, and discussions. Direct practice and learning by doing are preferred by kinesthetic learners (Becker et al., 2007 ; Fleming & Baume, 2006 ; Willingham et al., 2015 ). As a result, this research work aims to provide a comprehensive discussion about how these individual parameters can be applied in adaptive e-learning environment practices. Dominic et al., ( 2015 ) presented a framework for an adaptive educational system that personalized learning content based on student learning styles (Felder-Silverman learning model) and other factors such as learners' learning subject competency level. This framework allowed students to follow their adaptive learning content paths based on filling in "ils" questionnaire. Additionally, providing a customized framework that can automatically respond to students' learning styles and suggest online activities with complete personalization. Similarly, El Bachari et al. ( 2011 ) attempted to determine a student's unique learning style and then adapt instruction to that individual interests. Adaptive e-learning focused on learner experience and learning style has a higher degree of perceived usability than a non-adaptive e-learning system, according to Alshammari et al. ( 2015 ). This can also improve learners' satisfaction, engagement, and motivation, thus improving their learning.

According to the findings of (Akbulut & Cardak, 2012 ; Alshammari & Qtaish, 2019 ; Alzain et al., 2018a , b ; Shi et al., 2013 ; Truong, 2016 ), adaptation based on a combination of learning style, and information level yields significantly better learning gains. Researchers have recently initiated to focus on how to personalize e-learning experiences using personal characteristics such as the student's preferred learning style. Personal learning challenges are addressed by adaptive learning programs, which provide learners with courses that are fit to their specific needs, such as their learning styles.

  • Student engagement

Previous research has emphasized that student participation is a key factor in overcoming academic problems such as poor academic performance, isolation, and high dropout rates (Fredricks et al., 2004 ). Student participation is vital to student learning, especially in an online environment where students may feel isolated and disconnected (Dixson, 2015 ). Student engagement is the degree to which students consciously engage with a course's materials, other students, and the instructor. Student engagement is significant for keeping students engaged in the course and, as a result, in their learning (Barkley & Major, 2020 ; Lee et al., 2019 ; Rogers-Stacy, et al, 2017 ). Extensive research was conducted to investigate the degree of student engagement in web-based learning systems and traditional education systems. For instance, using a variety of methods and input features to test the relationship between student data and student participation (Hussain et al., 2018 ). Guo et al. ( 2014 ) checked the participation of students when they watched videos. The input characteristics of the study were based on the time they watched it and how often students respond to the assessment.

Atherton et al. ( 2017 ) found a correlation between the use of course materials and student performance; course content is more expected to lead to better grades. Pardo et al., ( 2016 ) found that interactive students with interactive learning activities have a significant impact on student test scores. The course results are positively correlated with student participation according to previous research. For example, Atherton et al. ( 2017 ) explained that students accessed learning materials online and passed exams regularly to obtain higher test scores. Other studies have shown that students with higher levels of participation in questionnaires and course performance tend to perform well (Mutahi et al., 2017 ).

Skills, emotion, participation, and performance, according to Dixson ( 2015 ), were factors in online learning engagement. Skills are a type of learning that includes things like practicing on a daily foundation, paying attention while listening and reading, and taking notes. Emotion refers to how the learner feels about learning, such as how much you want to learn. Participation refers to how the learner act in a class, such as chat, discussion, or conversation. Performance is a result, such as a good grade or a good test score. In general, engagement indicated that students spend time, energy learning materials, and skills to interact constructively with others in the classroom, and at least participate in emotional learning in one way or another (that is, be motivated by an idea, willing to learn and interact). Student engagement is produced through personal attitudes, thoughts, behaviors, and communication with others. Thoughts, effort, and feelings to a certain level when studying. Therefore, the student engagement scale attempts to measure what students are doing (thinking actively), how they relate to their learning, and how they relate to content, faculty members, and other learners including the following factors as shown in Fig.  2 . (skills, participation/interaction, performance, and emotions). Hence, previous research has moved beyond comparing online and face-to-face classes to investigating ways to improve online learning (Dixson, 2015 ; Gaytan & McEwen, 2007 ; Lévy & Wakabayashi, 2008 ; Mutahi et al., 2017 ). Learning effort, involvement in activities, interaction, and learning satisfaction, according to reviews of previous research on student engagement, are significant measures of student engagement in learning environments (Dixson, 2015 ; Evans et al., 2017 ; Lee et al., 2019 ; Mutahi et al., 2017 ; Rogers-Stacy et al., 2017 ). These results point to several features of e-learning environments that can be used as measures of student participation. Successful and engaged online learners learn actively, have the psychological inspiration to learn, make good use of prior experience, and make successful use of online technology. Furthermore, they have excellent communication abilities and are adept at both cooperative and self-directed learning (Dixson, 2015 ; Hong, 2009 ; Nkomo et al., 2021 ).

figure 2

Engagement factors

Overview of designing the adaptive e-learning environment

The paper follows the (ADDIE) Instructional Design Model: analysis, design, develop, implement, and evaluate to answer the first research question. The adaptive learning environment offers an interactive decentralized media environment that takes into account individual differences among students. Moreover, the environment can spread the culture of self-learning, attract students, and increase their engagement in learning.

Any learning environment that is intended to accomplish a specific goal should be consistent to increase students' motivation to learn. so that they have content that is personalized to their specific requirements, rather than one-size-fits-all content. As a result, a set of instructional design standards for designing an adaptive e-learning framework based on learning styles was developed according to the following diagram (Fig. 3 ).

figure 3

The ID (model) of the adaptive e-learning environment

According to the previous figure, The analysis phase included identifying the course materials and learning tools (syllabus and course plan modules) used for the study. The learning objectives were included in the high-level learning objectives (C4-C6: analysis, synthesis, evaluation).

The design phase included writing SMART objectives, the learning materials were written within the modules plan. To support adaptive learning, four content paths were identified, choosing learning models, processes, and evaluation. Course structure and navigation were planned. The adaptive structural design identified the relationships between the different components, such as introduction units, learning materials, quizzes. Determining the four path materials. The course instructional materials were identified according to the following Figure 4 .

figure 4

Adaptive e-course design

The development phase included: preparing and selecting the media for the e-course according to each content path in an adaptive e-learning environment. During this process, the author accomplished the storyboard and the media to be included on each page of the storyboard. A category was developed for the instructional media for each path (Fig. 5 )

figure 5

Roles and deployment diagram of the adaptive e-learning environment

The author developed a learning styles questionnaire via a mobile App. as follows: https://play.google.com/store/apps/details?id=com.pointability.vark . Then, the students accessed the adaptive e-course modules based on their learning styles.

The Implementation phase involved the following: The professional validation of the course instructional materials. Expert validation is used to evaluate the consistency of course materials (syllabi and modules). The validation was performed including the following: student learning activities, learning implementation capability, and student reactions to modules. The learner's behaviors, errors, navigation, and learning process are continuously geared toward improving the learner's modules based on the data the learner gathered about him.

The Evaluation phase included five e-learning specialists who reviewed the adaptive e-learning. After that, the framework was revised based on expert recommendations and feedback. Content assessment, media evaluation in three forms, instructional design, interface design, and usage design included in the evaluation. Adaptive learners checked the proposed framework. It was divided into two sections. Pilot testing where the proposed environment was tested by ten learners who represented the sample in the first phase. Each learner's behavior was observed, questions were answered, and learning control, media access, and time spent learning were all verified.

Research methodology

Research purpose and questions.

This research aims to investigate the impact of designing an adaptive e-learning environment on the development of students' engagement. The research conceptual framework is illustrated in Fig.  6 . Therefore, the articulated research questions are as follows: the main research question is "What is the impact of an adaptive e-learning environment based on (VARK) learning styles on developing students' engagement? Accordingly, there are two sub research questions a) "What is the instructional design of the adaptive e-learning environment?" b) "What is the impact of an adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation, performance, emotional) in comparison with conventional e-learning?".

figure 6

The conceptual framework (model) of the research questions

Research hypotheses

The research aims to verify the validity of the following hypothesis:

There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale.

There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group.

Research design

This research was a quasi-experimental research with the pretest-posttest. Research variables were independent and dependent as shown in the following Fig. 7 .

figure 7

Research "Experimental" design

Both groups were informed with the learning activities tracks, the experimental group was instructed to use the adaptive learning environment to accomplish the learning goals; on the other hand, the control group was exposed to the conventional e-learning environment without the adaptive e-learning parameters.

Research participants

The sample consisted of students studying the "learning skills" course in the common first-year deanship aged between (17–18) years represented the population of the study. All participants were chosen in the academic year 2109–2020 at the first term which was taught by the same instructors. The research sample included two classes (118 students), selected randomly from the learning skills department. First-group was randomly assigned as the control group (N = 58, 31 males and 27 females), the other was assigned as experimental group (N = 60, 36 males and 24 females) was assigned to the other class. The following Table 1 shows the distribution of students' sample "Demographics data".

The instructional materials were not presented to the students before. The control group was expected to attend the conventional e-learning class, where they were provided with the learning environment without adaptive e-learning parameter based on the learning styles that introduced the "learning skills" course. The experimental group was exposed to the use of adaptive e-learning based on learning styles to learn the same course instructional materials within e-course. Moreover, all the student participants were required to read the guidelines to indicate their readiness to participate in the research experiment with permission.

Research instruments

In this research, the measuring tools included the VARK questionnaire and the students' engagement scale including the following factors (skills, participation/interaction, performance, emotional). To begin, the pre-post scale was designed to assess the level of student engagement related to the "learning skills" course before and after participating in the experiment.

VARK questionnaire

Questionnaires are a common method for collecting data in education research (McMillan & Schumacher, 2006 ). The VARK questionnaire had been organized electronically and distributed to the student through the developed mobile app and registered on the UQU system. The questionnaire consisted of 16 items within the scale as MCQ classified into four main factors (kinesthetic, auditory, visual, and R/W).

Reliability and Validity of The VARK questionnaire

For reliability analysis, Cronbach’s alpha is used for evaluating research internal consistency. Internal consistency was calculated through the calculation of correlation of each item with the factor to which it fits and correlation among other factors. The value of 0.70 and above are normally recognized as high-reliability values (Hinton et al., 2014 ). The Cronbach's Alpha correlation coefficient for the VARK questionnaire was 0.83, indicating that the questionnaire was accurate and suitable for further research.

Students' engagement scale

The engagement scale was developed after a review of the literature on the topic of student engagement. The Dixson scale was used to measure student engagement. The scale consisted of 4 major factors as follows (skills, participation/interaction, performance, emotional). The author adapted the original "Dixson scale" according to the following steps. The Dixson scale consisted of 48 statements was translated and accommodated into Arabic by the author. After consulting with experts, the instrument items were reduced to 27 items after adaptation according to the university learning environment. The scale is rated on a 5-point scale.

The final version of the engagement scale comprised 4 factors as follows: The skills engagement included (ten items) to determine keeping up with, reading instructional materials, and exerting effort. Participation/interaction engagement involved (five items) to measure having fun, as well as regularly engaging in group discussion. The performance engagement included (five items) to measure test performance and receiving a successful score. The emotional engagement involved (seven items) to decide whether or not the course was interesting. Students can access to respond engagement scale from the following link: http://bit.ly/2PXGvvD . Consequently, the objective of the scale is to measure the possession of common first-year students of the basic engagement factors before and after instruction with adaptive e-learning compared to conventional e-learning.

Reliability and validity of the engagement scale

The alpha coefficient of the scale factors scores was presented. All four subscales have a strong degree of internal accuracy (0.80–0.87), indicating strong reliability. The overall reliability of the instruments used in this study was calculated using Alfa-alpha, Cronbach's with an alpha value of 0.81 meaning that the instruments were accurate. The instruments used in this research demonstrated strong validity and reliability, allowing for an accurate assessment of students' engagement in learning. The scale was applied to a pilot sample of 20 students, not including the experimental sample. The instrument, on the other hand, had a correlation coefficient of (0.74–0.82), indicating a degree of validity that enables the instrument's use. Table 2 shows the correlation coefficient and Cronbach's alpha based on the interaction scale.

On the other hand, to verify the content validity; the scale was to specialists to take their views on the clarity of the linguistic formulation and its suitability to measure students' engagement, and to suggest what they deem appropriate in terms of modifications.

Research procedures

To calculate the homogeneity and group equivalence between both groups, the validity of the first hypothesis was examined which stated "There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale", the author applied the engagement scale to both groups beforehand, and the scores of the pre-application were examined to verify the equivalence of the two groups (experimental and control) in terms of students' engagement.

The t-test of independent samples was calculated for the engagement scale to confirm the homogeneity of the two classes before the experiment. The t-values were not significant at the level of significance = 0.05, meaning that the two groups were homogeneous in terms of students' engagement scale before the experiment.

Since there was no significant difference in the mean scores of both groups ( p  > 0.05), the findings presented in Table 3 showed that there was no significant difference between both experimental and control groups in engagement as a whole, and each student engagement factor separately. The findings showed that the two classes were similar before start of research experiment.

Learner content path in adaptive e-learning environment

The previous well-designed processes are the foundation for adaptation in e-learning environments. There are identified entries for accommodating materials, including classification depending on learning style.: kinesthetic, auditory, visual, and R/W. The present study covered the 1st semester during the 2019/2020 academic year. The course was divided into modules that concentrated on various topics; eleven of the modules included the adaptive learning exercise. The exercises and quizzes were assigned to specific textbook modules. To reduce irrelevant variation, all objects of the course covered the same content, had equal learning results, and were taught by the same instructor.

The experimental group—in which students were asked to bring smartphones—was taught, where the how-to adaptive learning application for adaptive learning was downloaded, and a special account was created for each student, followed by access to the channel designed by the through the application, and the students were provided with instructions and training on how entering application with the appropriate default element of the developed learning objects, while the control group used the variety of instructional materials in the same course for the students.

In this adaptive e-course, students in the experimental group are presented with a questionnaire asked to answer that questions via a developed mobile App. They are provided with four choices. Students are allowed to answer the questions. The correct answer is shown in the students' responses to the results, but the learning module is marked as incomplete. If a student chooses to respond to a question, the correct answer is found immediately, regardless of the student's reaction.

Figure  8 illustrates a visual example from learning styles identification through responding VARK Questionnaire. The learning process experienced by the students in this adaptive Learning environment is as shown in Fig.  4 . Students opened the adaptive course link by tapping the following app " https://play.google.com/store/apps/details?id=com.pointability.vark ," which displayed the appropriate positioning of both the learning skills course and the current status of students. It directed students to the learning skills that they are interested in learning more. Once students reached a specific situation in the e-learning environment, they could access relevant digital instructional materials. Students were then able to progress through the various styles offered by the proposed method, giving them greater flexibility in their learning pace.

figure 8

Visual example from "learning of the learning styles" identification and adaptive e-learning course process

The "flowchart" diagram below illustrates the learner's path in an adaptive e-learning environment, depending on the (VARK) learning styles (visual, auditory, kinesthetic, reading/writing) (Fig. 9 ).

figure 9

Student learning path

According to the previous design model of the adaptive framework, the students responded "Learning Styles" questionnaire. Based on each student's results, the orientation of students will direct to each of "Visual", "Aural", "Read-Write", and "Kinesthetic". The student took at the beginning the engagement scale online according to their own pace. When ready, they responded "engagement scale".

Based on the results, the system produced an individualized learning plan to fill in the gap based on the VARK questionnaire's first results. The learner model represents important learner characteristics such as personal information, knowledge level, and learning preferences. Pre and post measurements were performed for both experimental and control groups. The experimental group was exposed only to treatment (using the adaptive learning environment).

To address the second question, which states: “What is the impact "effect" of adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation/interaction, performance, emotional) in comparison with conventional e-learning?

The validity of the second hypothesis of the research hypothesis was tested, which states " There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group". To test the hypothesis, the arithmetic means, standard deviations, and "T"-test values were calculated for the results of the two research groups in the application of engagement scale factors".

Table 4 . indicates that students in the experimental group had significantly higher mean of engagement post-test (engagement factors items) scores than students in the control group ( p  < 0.05).

The experimental research was performed to evaluate the impact of the proposed adaptive e-learning. Independent sample t-tests were used to measure the previous behavioral engagement of the two groups related to topic of this research. Subsequently, the findings stated that the experimental group students had higher learning achievement than those who were taught using the conventional e-learning approach.

To verify the effect size of the independent variable in terms of the dependent variable, Cohen (d) was used to investigate that adaptive learning can significantly students' engagement. According to Cohen ( 1992 ), ES of 0.20 is small, 0.50 is medium, and 0.80 is high. In the post-test of the student engagement scale, however, the effect size between students' scores in the experimental and control groups was calculated using (d and r) using means and standard deviations. Cohen's d = 0.826, and Effect-size r = 0.401, according to the findings. The ES of 0.824 means that the treated group's mean is in the 79th percentile of the control group (Large effect). Effect sizes can also be described as the average percentile rank of the average treated learner compared to the average untreated learner in general. The mean of the treated group is at the 50th percentile of the untreated group, indicating an ES of 0.0. The mean of the treated group is at the 79th percentile of the untreated group, with an ES of 0.8. The results showed that the dependent variable was strongly influenced in the four behavioral engagement factors: skills: performance, participation/interaction, and emotional, based on the fact that effect size is a significant factor in determining the research's strength.

Discussions and limitations

This section discusses the impact of an adaptive e-learning environment on student engagement development. This paper aimed to design an adaptive e-learning environment based on learning style parameters. The findings revealed that factors correlated to student engagement in e-learning: skills, participation/interaction, performance, and emotional. The engagement factors are significant because they affect learning outcomes (Nkomo et al., 2021 ). Every factor's items correlate to cognitive process-related activities. The participation/interaction factor, for example, referred to, interactions with the content, peers, and instructors. As a result, student engagement in e-learning can be predicted by interactions with content, peers, and instructors. The results are in line with previous research, which found that customized learning materials are important for increasing students' engagement. Adaptive e-learning based on learning styles sets a strong emphasis on behavioral engagement, in which students manage their learning while actively participating in online classes to adapt instruction according to each learning style. This leads to improved learning outcomes (Al-Chalabi & Hussein, 2020 ; Chun-Hui et al., 2017 ; Hussein & Al-Chalabi, 2020 ; Pashler et al., 2008 ). The experimental findings of this research showed that students who learned through adaptive eLearning based on learning styles learned more; as learning styles are reflected in this research as one of the generally assumed concerns as a reference for adapting e-content path. Students in the experimental group reported that the adaptive eLearning environment was very interesting and able to attract their attention. Those students also indicated that the adaptive eLearning environment was particularly useful because it provided opportunities for them to recall the learning content, thus enhancing their overall learning impression. This may explain why students in the experimental group performed well in class and showed more enthusiasm than students in the control group. This research compared an adaptive e-learning environment to a conventional e-learning approach toward engagement in a learning skills course through instructional content delivery and assessment. It can also be noticed that the experimental group had higher participation than the control group, indicating that BB activities were better adapted to the students' learning styles. Previous studies have agreed on the effectiveness of adaptive learning; it provides students with quality opportunity that is adapted to their learning styles, and preferences (Alshammari, 2016 ; Hussein & Al-Chalabi, 2020 ; Roy & Roy, 2011 ; Surjono, 2014 ). However, it should be noted that this study is restricted to one aspect of content adaptation and its factors, which is learning materials adapting based on learning styles. Other considerations include content-dependent adaptation. These findings are consistent with other studies, such as (Alshammari & Qtaish, 2019 ; Chun-Hui et al., 2017 ), which have revealed the effectiveness of the adaptive e-learning environment. This research differs from others in that it reflects on the Umm Al-Qura University as a case study, VARK Learning styles selection, engagement factors, and the closed learning management framework (BB).

The findings of the study revealed that adaptive content has a positive impact on adaptive individuals' achievement and student engagement, based on their learning styles (kinesthetic; auditory; visual; read/write). Several factors have contributed to this: The design of adaptive e-content for learning skills depended on introducing an ideal learning environment for learners, and providing support for learning adaptation according to the learning style, encouraging them to learn directly, achieving knowledge building, and be enjoyable in the learning process. Ali et al. ( 2019 ) confirmed that, indicating that education is adapted according to each individual's learning style, needs, and characteristics. Adaptive e-content design that allows different learners to think about knowledge by presenting information and skills in a logical sequence based on the adaptive e-learning framework, taking into account its capabilities as well as the diversity of its sources across the web, and these are consistent with the findings of (Alshammari & Qtaish, 2019 ).

Accordingly, the previous results are due to the following: good design of the adaptive e-learning environment in light of the learning style and educational preferences according to its instructional design (ID) standards, and the provision of adaptive content that suits the learners' needs, characteristics, and learning style, in addition to the diversity of course content elements (texts, static images, animations, and video), variety of tests and activities, diversity of methods of reinforcement, return and support from the instructor and peers according to the learning style, as well as it allows ease of use, contains multiple and varied learning sources, and allows referring to the same point when leaving the environment.

Several studies have shown that using adaptive eLearning technologies allows students to improve their learning knowledge and further enhance their engagement in issues such as "skills, performance, interaction, and emotional" (Ali et al., 2019 ; Graf & Kinshuk, 2007 ; Murray & Pérez, 2015 ); nevertheless, Murray and Pérez ( 2015 ) revealed that adaptive learning environments have a limited impact on learning outcome.

The restricted empirical findings on the efficacy of adapting teaching to learning style are mixed. (Chun-Hui et al., 2017 ) demonstrated that adaptive eLearning technologies can be beneficial to students' learning and development. According to these findings, adaptive eLearning can be considered a valuable method for learning because it can attract students' attention and promote their participation in educational activities. (Ali et al., 2019 ); however, only a few recent studies have focused on how adaptive eLearning based on learning styles fits in diverse cultural programs. (Benhamdi et al., 2017 ; Pashler et al., 2008 ).

The experimental results revealed that the proposed environment significantly increased students' learning achievements as compared to the conventional e-learning classroom (without adaptive technology). This means that the proposed environment's adaptation could increase students' engagement in the learning process. There is also evidence that an adaptive environment positively impacts other aspects of quality such as student engagement (Murray & Pérez, 2015 ).

Conclusions and implications

Although this field of research has stimulated many interests in recent years, there are still some unanswered questions. Some research gaps are established and filled in this study by developing an active adaptive e-learning environment that has been shown to increase student engagement. This study aimed to design an adaptive e-learning environment for performing interactive learning activities in a learning skills course. The main findings of this study revealed a significant difference in learning outcomes as well as positive results for adaptive e-learning students, indicating that it may be a helpful learning method for higher education. It also contributed to the current adaptive e-learning literature. The findings revealed that adaptive e-learning based on learning styles could help students stay engaged. Consequently, adaptive e-learning based on learning styles increased student engagement significantly. According to research, each student's learning style is unique, and they prefer to use different types of instructional materials and activities. Furthermore, students' preferences have an impact on the effectiveness of learning. As a result, the most effective learning environment should adjust its output to the needs of the students. The development of high-quality instructional materials and activities that are adapted to students' learning styles will help them participate and be more motivated. In conclusion, learning styles are a good starting point for creating instructional materials based on learning theories.

This study's results have important educational implications for future studies on the effect of adaptive e-learning on student interaction. First, the findings may provide data to support the development and improvement of adaptive environments used in blended learning. Second, the results emphasize the need for more quasi-experimental and descriptive research to better understand the benefits and challenges of incorporating adaptive e-learning in higher education institutions. Third, the results of this study indicate that using an adaptive model in an adaptive e-learning environment will encourage, motivate, engage, and activate students' active learning, as well as facilitate their knowledge construction, rather than simply taking in information passively. Fourth, new research is needed to design effective environments in which adaptive learning can be used in higher education institutions to increase academic performance and motivation in the learning process. Finally, the study shows that adaptive e-learning allows students to learn individually, which improves their learning and knowledge of course content, such as increasing their knowledge of learning skills course topics beyond what they can learn in a conventional e-learning classroom.

Contribution to research

The study is intended to provide empirical evidence of adaptive e-learning on student engagement factors. This research, on the other hand, has practical implications for higher education stakeholders, as it is intended to provide university faculty members with learning approaches that will improve student engagement. It is also expected to offer faculty a framework for designing personalized learning environments based on learning styles in various learning situations and designing more adaptive e-learning environments.

Research implication

Students with their preferred learning styles are more likely to enjoy learning if they are provided with a variety of instructional materials such as references, interactive media, videos, podcasts, storytelling, simulation, animation, problem-solving, games, and accessible educational tools in an e-learning environment. Also, different learning strategies can be accommodated. Other researchers would be able to conduct future studies on the use of the "adaptive e-learning" approach throughout the instructional process, at different phases of learning, and in various e-courses as a result of the current study. Meanwhile, the proposed environment's positive impact on student engagement gained considerable interest for future educational applications. Further research on learning styles in different university colleges could contribute to a foundation for designing adaptive e-courses based on students' learning styles and directing more future research on learning styles.

Implications for practice or policy:

Adaptive e-learning focused on learning styles would help students become more engaged.

Proving the efficacy of an adaptive e-learning environment via comparison with conventional e-learning .

Availability of data and materials

The author confirms that the data supporting the findings of this study are based on the research tools which were prepared and explained by the author and available on the links stated in the research instruments sub-section. The data analysis that supports the findings of this study is available on request from the corresponding author.

Akbulut, Y., & Cardak, C. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education . https://doi.org/10.1016/j.compedu.2011.10.008 .

Article   Google Scholar  

Al-Chalabi, H., & Hussein, A. (2020). Analysis & implementation of personalization parameters in the development of computer-based adaptive learning environment. SAR Journal Science and Research., 3 (1), 3–9. https://doi.org/10.18421//SAR31-01 .

Aldosari, M., Aljabaa, A., Al-Sehaibany, F., & Albarakati, S. (2018). Learning style preferences of dental students at a single institution in Riyadh Saudi Arabia, evaluated using the VARK questionnaire . Advances in Medical Education and Practice. https://doi.org/10.2147/AMEP.S157686 .

Ali, N., Eassa, F., & Hamed, E. (2019). Personalized Learning Style for Adaptive E-Learning System, International Journal of Advanced Trends in Computer Science and Engineering . 223-230. Retrieved June 26, 2020 from http://www.warse.org/IJATCSE/static/pdf/file/ijatcse4181.12019.pdf .

Alshammari, M., & Qtaish, A. (2019). Effective adaptive e-learning systems according to learning style and knowledge level. JITE Research, 18 , 529–547. https://doi.org/10.28945/4459 .

Alshammari, M. (2016). Adaptation based on learning style and knowledge level in e-learning systems, Ph.D. thesis , University of Birmingham.  Retrieved April 18, 2019 from http://etheses.bham.ac.uk//id/eprint/6702/ .

Alshammari, M., Anane, R., & Hendley, R. (2015). Design and Usability Evaluation of Adaptive E-learning Systems based on Learner Knowledge and Learning Style. Human-Computer Interaction Conference- INTERACT , Vol. (9297), (pp. 157–186). https://doi.org/10.1007/978-3-319-22668-2_45 .

Alzain, A., Clack, S., Jwaid, A., & Ireson, G. (2018a). Adaptive education based on learning styles: Are learning style instruments precise enough. International Journal of Emerging Technologies in Learning (iJET), 13 (9), 41–52. https://doi.org/10.3991/ijet.v13i09.8554 .

Alzain, A., Clark, S., Ireson, G., & Jwaid, A. (2018b). Learning personalization based on learning style instruments. Advances in Science Technology and Engineering Systems Journal . https://doi.org/10.25046/aj030315 .

Atherton, M., Shah, M., Vazquez, J., Griffiths, Z., Jackson, B., & Burgess, C. (2017). Using learning analytics to assess student engagement and academic outcomes in open access enabling programs”. Journal of Open, Distance and e-Learning, 32 (2), 119–136.

Barkley, E., & Major, C. (2020). Student engagement techniques: A handbook for college faculty . Jossey-Bass . 10:047028191X.

Google Scholar  

Becker, K., Kehoe, J., & Tennent, B. (2007). Impact of personalized learning styles on online delivery and assessment. Campus-Wide Information Systems . https://doi.org/10.1108/10650740710742718 .

Behaz, A., & Djoudi, M. (2012). Adaptation of learning resources based on the MBTI theory of psychological types. IJCSI International Journal of Computer Science, 9 (2), 135–141.

Beldagli, B., & Adiguzel, T. (2010). Illustrating an ideal adaptive e-learning: A conceptual framework. Procedia - Social and Behavioral Sciences, 2 , 5755–5761. https://doi.org/10.1016/j.sbspro.2010.03.939 .

Benhamdi, S., Babouri, A., & Chiky, R. (2017). Personalized recommender system for e-Learning environment. Education and Information Technologies, 22 , 1455–1477. https://doi.org/10.1007/s10639-016-9504-y .

Chen, P., Lambert, A., & Guidry, K. (2010). Engaging online learners: The impact of Web-based learning technology on college student engagement. Computers & Education, 54 , 1222–1232.

Chun-Hui, Wu., Chen, Y.-S., & Chen, T. C. (2017). An adaptive e-learning system for enhancing learning performance: based on dynamic scaffolding theory. Eurasia Journal of Mathematics, Science and Technology Education. https://doi.org/10.12973/ejmste/81061 .

Cletus, D., & Eneluwe, D. (2020). The impact of learning style on student performance: mediate by personality. International Journal of Education, Learning and Training. https://doi.org/10.24924/ijelt/2019.11/v4.iss2/22.47Desmond .

Cohen, J. (1992). Statistical power analysis. Current Directions in Psychological Science., 1 (3), 98–101. https://doi.org/10.1111/1467-8721.ep10768783 .

Daines, J., Troka, T. and Santiago, J. (2016). Improving performance in trigonometry and pre-calculus by incorporating adaptive learning technology into blended models on campus. https://doi.org/10.18260/p.25624 .

DeCapua, A. & Marshall, H. (2015). Implementing a Mutually Adaptive Learning Paradigm in a Community-Based Adult ESL Literacy Class. In M. Santos & A. Whiteside (Eds.). Low Educated Second Language and Literacy Acquisition. Proceedings of the Ninth Symposium (pps. 151-171). Retrieved Nov. 14, 2020 from https://www.researchgate.net/publication/301355138_Implementing_a_Mutually_Adaptive_Learning_Paradigm_in_a_Community-Based_Adult_ESL_Literacy_Class .

Dixson, M. (2015). Measuring student engagement in the online course: The online student engagement scale (OSE). Online Learning . https://doi.org/10.24059/olj.v19i4.561 .

Dominic, M., Xavier, B., & Francis, S. (2015). A Framework to Formulate Adaptivity for Adaptive e-Learning System Using User Response Theory. International Journal of Modern Education and Computer Science, 7 , 23. https://doi.org/10.5815/ijmecs.2015.01.04 .

El Bachari, E., Abdelwahed, E., & M., El. . (2011). E-Learning personalization based on Dynamic learners’ preference. International Journal of Computer Science and Information Technology., 3 , 200–216. https://doi.org/10.5121/ijcsit.2011.3314 .

El-Sabagh, H. A., & Hamed, E. (2020). The Relationship between Learning-Styles and Learning Motivation of Students at Umm Al-Qura University. Egyptian Association for Educational Computer Journal . https://doi.org/10.21608/EAEC.2020.25868.1015 ISSN-Online: 2682-2601.

Ennouamani, S., & Mahani, Z. (2017). An overview of adaptive e-learning systems. Eighth International ConfeRence on Intelligent Computing and Information Systems (ICICIS) . https://doi.org/10.1109/INTELCIS.2017.8260060 .

Evans, S., Steele, J., Robertson, S., & Dyer, D. (2017). Personalizing post titles in the online classroom: A best practice? Journal of Educators Online, 14 (2), 46–54.

Fleming, N., & Baume, D. (2006). Learning styles again: VARKing up the Right Tree! Educational Developments, 7 , 4–7.

Franzoni, A., & Assar, S. (2009). Student learning style adaptation method based on teaching strategies and electronic media. Journal of Educational Technology & Society , 12(4), 15–29. Retrieved March 21, 2020, from http://www.jstor.org/stable/jeductechsoci.12.4.15 .

Fredricks, J., Blumenfeld, P., & Paris, A. (2004). School Engagement: Potential of the Concept . State of the Evidence: Review of Educational Research. https://doi.org/10.3102/00346543074001059 .

Book   Google Scholar  

Gaytan, J., & McEwen, M. (2007). Effective Online Instructional and Assessment Strategies. American Journal of Distance Education, 21 (3), 117–132. https://doi.org/10.1080/08923640701341653 .

Graf, S. & Kinshuk. K. (2007). Providing Adaptive Courses in Learning Management Systems with respect to Learning Styles. Proceeding of the World Conference on eLearning in Corporate. Government. Healthcare. and Higher Education (2576–2583). Association for the Advancement of Computing in Education (AACE). Retrieved January 18, 2020 from  https://www.learntechlib.org/primary/p/26739/ . ISBN 978-1-880094-63-1.

Guo, P., Kim, V., & Rubin, R. (2014). How video production affects student engagement: an empirical study of MOOC videos. Proceedings of First ACM Conference on Learning @ Scale Confernce . March 2014, (pp. 41-50). https://doi.org/10.1145/2556325.2566239 .

Hinton, P. R., Brownlow, C., McMurray, I., & Cozens, B. (2014). SPSS Explained (2nd ed., pp. 339–354). Routledge Taylor & Francis Group.

Hong, S. (2009). Developing competency model of learners in distance universities. Journal of Educational Technology., 25 , 157–186.

Hussain, I. (2017). Pedagogical implications of VARK model of learning. Journal of Literature, Languages and Linguistics, 38 , 33–37.

Hussain, M., Zhu, W., Zhang, W., & Abidi, S. (2018). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence, and Neuroscience. https://doi.org/10.1155/2018/6347186 .

Hussein, A., & Al-Chalabi, H. (2020). Pedagogical Agents in an Adaptive E-learning System. SAR Journal of Science and Research., 3 , 24–30. https://doi.org/10.18421/SAR31-04 .

Jaleel, S., & Thomas, A. (2019). Learning styles theories and implications for teaching learning . Horizon Research Publishing. 978-1-943484-25-6.

Johnson, M. (2009). Evaluation of Learning Style for First-Year Medical Students. Int J Schol Teach Learn . https://doi.org/10.20429/ijsotl.2009.030120 .

Jonassen, D. H., & Grabowski, B. L. (2012). Handbook of individual differences, learning, and instruction. Routledge . https://doi.org/10.1016/0022-4405(95)00013-C .

Klasnja-Milicevic, A., Vesin, B., Ivanovic, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56 (3), 885–899. https://doi.org/10.1016/j.compedu.2010.11.001 .

Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2017). Prediction of learner’s profile based on learning styles in adaptive e-learning system. International Journal of Emerging Technologies in Learning, 12 (6), 31–51. https://doi.org/10.3991/ijet.v12i06.6579 .

Lee, J., & Kim, D. (2012). Adaptive learning system applied bruner’ EIS theory. International Conference on Future Computer Supported Education, IERI Procedia, 2 , 794–801. https://doi.org/10.1016/j.ieri.2012.06.173 .

Lee, J., Song, H.-D., & Hong, A. (2019). Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning. Sustainability, 11 , 985. https://doi.org/10.3390/su11040985 .

Leung, A., McGregor, M., Sabiston, D., & Vriliotis, S. (2014). VARK learning styles and student performance in principles of Micro-vs. Macro-Economics. Journal of Economics and Economic Education Research, 15 (3), 113.

Lévy, P. & Wakabayashi, N. (2008). User's appreciation of engagement in service design: The case of food service design. Proceedings of International Service Innovation Design Conference 2008 - ISIDC08 . Busan, Korea. Retrieved October 28, 2019 from https://www.researchgate.net/publication/230584075 .

Liang, J. S. (2012). The effects of learning styles and perceptions on application of interactive learning guides for web-based. Proceedings of Australasian Association for Engineering Education Conference AAEE . Melbourne, Australia. Retrieved October 22, 2019 from https://aaee.net.au/wpcontent/uploads/2018/10/AAEE2012-Liang.-Learning_styles_and_perceptions_effects_on_interactive_learning_guide_application.pdf .

Mahnane, L., Laskri, M. T., & Trigano, P. (2013). A model of adaptive e-learning hypermedia system based on thinking and learning styles. International Journal of Multimedia and Ubiquitous Engineering, 8 (3), 339–350.

Markey, M. K. & Schmit, K, J. (2008). Relationship between learning style Preference and instructional technology usage. Proceedings of American Society for Engineering Education Annual Conference & Expodition . Pittsburgh, Pennsylvania. Retrieved March 15, 2020 from https://peer.asee.org/3173 .

McMillan, J., & Schumacher, S. (2006). Research in education: Evidence-based inquiry . Pearson.

Murphy, R., Gray, S., Straja, S., & Bogert, M. (2004). Student learning preferences and teaching implications: Educational methodologies. Journal of Dental Education, 68 (8), 859–866.

Murray, M., & Pérez, J. (2015). Informing and performing: A study comparing adaptive learning to traditional learning. Informing Science. The International Journal of an Emerging Transdiscipline , 18, 111–125. Retrieved Febrauary 4, 2021 from http://www.inform.nu/Articles/Vol18/ISJv18p111-125Murray1572.pdf .

Mutahi, J., Kinai, A. , Bore, N. , Diriye, A. and Weldemariam, K. (2017). Studying engagement and performance with learning technology in an African classroom, Proceedings of Seventh International Learning Analytics & Knowledge Conference , (pp. 148–152), Canada: Vancouver.

Nainie, Z., Siraj, S., Abuzaiad, R. A., & Shagholi, R. (2010). Hypothesized learners’ technology preferences based on learning styles dimensions. The Turkish Online Journal of Educational Technology, 9 (4), 83–93.

Naqeeb, H. (2011). Learning Styles as Perceived by Learners of English as a Foreign Language in the English Language Center of The Arab American University—Jenin. Palestine. an Najah Journal of Research, 25 , 2232.

Nkomo, L. M., Daniel, B. K., & Butson, R. J. (2021). Synthesis of student engagement with digital technologies: a systematic review of the literature. International Journal of Educational Technology in Higher Education . https://doi.org/10.1186/s41239-021-00270-1 .

Normadhi, N. B., Shuib, L., Nasir, H. N. M., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education, 130 , 168–190. https://doi.org/10.1016/j.compedu.2018.11.005 .

Nuankaew, P., Nuankaew, W., Phanniphong, K., Imwut, S., & Bussaman, S. (2019). Students model in different learning styles of academic achievement at the University of Phayao, Thailand. International Journal of Emerging Technologies in Learning (iJET)., 14 , 133. https://doi.org/10.3991/ijet.v14i12.10352 .

Oxman, S. & Wong, W. (2014). White Paper: Adaptive Learning Systems. DV X Innovations DeVry Education Group. Retrieved December 14, 2020 from shorturl.at/hnsS8 .

Ozyurt, Ö., & Ozyurt, H. (2015). Learning style-based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014. Computers in Human Behavior, 52 , 349–358. https://doi.org/10.1016/j.chb.2015.06.020 .

Pardo, A., Han, F., & Ellis, R. (2016). Exploring the relation between self-regulation, online activities, and academic performance: a case study. Proceedings of Sixth International Conference on Learning Analytics & Knowledge , (pp. 422-429). https://doi.org/10.1145/2883851.2883883 .

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: concepts and evidence. Psychology Faculty Publications., 9 (3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x .

Qazdar, A., Cherkaoui, C., Er-Raha, B., & Mammass, D. (2015). AeLF: Mixing adaptive learning system with learning management system. International Journal of Computer Applications., 119 , 1–8. https://doi.org/10.5120/21140-4171 .

Robinson, C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84 , 101–109.

Rogers-Stacy, C., Weister, T., & Lauer, S. (2017). Nonverbal immediacy behaviors and online student engagement: Bringing past instructional research into the present virtual classroom. Communication Education, 66 (1), 37–53.

Roy, S., & Roy, D. (2011). Adaptive e-learning system: a review. International Journal of Computer Trends and Technology (IJCTT), 1 (1), 78–81. ISSN:2231-2803.

Shi, L., Cristea, A., Foss, J., Qudah, D., & Qaffas, A. (2013). A social personalized adaptive e-learning environment: a case study in topolor. IADIS International Journal on WWW/Internet., 11 , 13–34.

Shih, M., Feng, J., & Tsai, C. (2008). Research and trends in the field of e-learning from 2001 to 2005: A content analysis of cognitive studies in selected journals. Computers & Education, 51 (2), 955–967. https://doi.org/10.1016/j.compedu.2007.10.004 .

Silva, A. (2020). Towards a Fuzzy Questionnaire of Felder and Solomon for determining learning styles without dichotomic in the answers. Journal of Learning Styles, 13 (15), 146–166.

Staikopoulos, A., Keeffe, I., Yousuf, B. et al., (2015). Enhancing student engagement through personalized motivations. Proceedings of IEEE 15th International Conference on Advanced Learning Technologies , (pp. 340–344), Taiwan: Hualien. https://doi.org/10.1109/ICALT.2015.116 .

Surjono, H. D. (2014). The evaluation of Moodle-based adaptive e-learning system. International Journal of Information and Education Technology, 4 (1), 89–92. https://doi.org/10.7763/IJIET.2014.V4.375 .

Truong, H. (2016). Integrating learning styles and adaptive e-learning system: current developments, problems, and opportunities. Computers in Human Behavior, 55 (2016), 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014 .

Umm Al-Qura University Agency for Educational Affairs (2020). Common first-year Deanship, at Umm Al-Qura University. Retrieved February 3, 2020 from https://uqu.edu.sa/en/pre-edu/70021 .

Vassileva, D. (2012). Adaptive e-learning content design and delivery based on learning style and knowledge level. Serdica Journal of Computing, 6 , 207–252.

Veiga, F., Robu, V., Appleton, J., Festas, I & Galvao, D. (2014). Students' engagement in school: Analysis according to self-concept and grade level. Proceedings of EDULEARN14 Conference 7th-9th July 2014 (pp. 7476-7484). Barcelona, Spain. Available Online at: http://hdl.handle.net/10451/12044 .

Velázquez, A., & Assar, S. (2009). Student learning styles adaptation method based on teaching strategies and electronic media. Educational Technology & SocieTy., 12 , 15–29.

Verdú, E., Regueras, L., & De Castro, J. (2008). An analysis of the research on adaptive Learning: The next generation of e-learning. WSEAS Transactions on Information Science and Applications, 6 (5), 859–868.

Willingham, D., Hughes, E., & Dobolyi, D. (2015). The scientific status of learning styles theories. Teaching of Psychology., 42 (3), 266–271. https://doi.org/10.1177/0098628315589505 .

Yalcinalp & Avcı. (2019). Creativity and emerging digital educational technologies: A systematic review. The Turkish Online Journal of Educational Technology, 18 (3), 25–45.

Yang, J., Huang, R., & Li, Y. (2013). Optimizing classroom environment to support technology enhanced learning. In A. Holzinger & G. Pasi (Eds.), Human-computer interaction and knowledge discovery in complex (pp. 275–284). Berlin: Springer.

Zhang, H. (2017). Accommodating different learning styles in the teaching of economics: with emphasis on fleming and mills¡¯s sensory-based learning style typology. Applied Economics and Finance, 4 (1), 72–78.

Download references


The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for the continuous support. This work was supported financially by the Deanship of Scientific Research at Umm Al-Qura University to Dr.: Hassan Abd El-Aziz El-Sabagh. (Grant Code: 18-EDU-1-01-0001).

Author information

Hassan A. El-Sabagh is an assistant professor in the E-Learning Deanship and head of the Instructional Programs Department, Umm Al-Qura University, Saudi Arabia, where he has worked since 2012. He has extensive experience in the field of e-learning and educational technologies, having served primarily at the Educational Technology Department of the Faculty of Specific Education, Mansoura University, Egypt since 1997. In 2011, he earned a Ph.D. in Educational Technology from Dresden University of Technology, Germany. He has over 14 papers published in international journals/conference proceedings, as well as serving as a peer reviewer in several international journals. His current research interests include eLearning Environments Design, Online Learning; LMS-based Interactive Tools, Augmented Reality, Design Personalized & Adaptive Learning Environments, and Digital Education, Quality & Online Courses Design, and Security issues of eLearning Environments. (E-mail: [email protected]; [email protected]).

Authors and Affiliations

E-Learning Deanship, Umm Al-Qura University, Mecca, Saudi Arabia

Hassan A. El-Sabagh

Faculty of Specific Education, Mansoura University, Mansoura, Egypt

You can also search for this author in PubMed   Google Scholar


The author read and approved the final manuscript.

Corresponding author

Correspondence to Hassan A. El-Sabagh .

Ethics declarations

Competing interests.

The author declares that there is no conflict of interest

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and Permissions

About this article

Cite this article.

El-Sabagh, H.A. Adaptive e-learning environment based on learning styles and its impact on development students' engagement. Int J Educ Technol High Educ 18 , 53 (2021). https://doi.org/10.1186/s41239-021-00289-4

Download citation

Received : 24 May 2021

Accepted : 19 July 2021

Published : 01 October 2021

DOI : https://doi.org/10.1186/s41239-021-00289-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Adaptive e-Learning
  • Learning style
  • Learning impact

learning styles research articles


  1. different learning styles and tips for teaching

    learning styles research articles

  2. The 7 Most Common Learning Types [Infographic]

    learning styles research articles

  3. Learning Styles And Research Methods Connection (Pdf)

    learning styles research articles

  4. 1. The Use of Learning Theories and Styles in Classical Studies (and Junior English

    learning styles research articles

  5. Teaching and learning styles research

    learning styles research articles

  6. (PDF) Understanding the Learning Styles and its Influence on Teaching/Learning Process

    learning styles research articles


  1. Learning styles #studymotivation #studytips #learningvideos ng

  2. learning styles

  3. Discover your learning Style| Four different learning styles #learningstyles #specialneeds

  4. Tips on how to develop an interesting research topic for graduate students

  5. types of learning styles

  6. Learning Styles


  1. The Best Ways to Style Men’s Ralph Lauren Clothing

    Looking to up your style game? It can help to learn how to style men’s Ralph Lauren clothing and look your best. There’s a variety of ways to achieve the look you want, and this article will provide you with the tips and tricks needed to ge...

  2. What Is a News Feature?

    A news feature is a type of feature story written in the style of a news article. It tackles a topic with painstaking detail and requires facts and research to back the story. News features are increasingly found in publications, as they ar...

  3. Make Learning a Habit: Find the Best Daily Articles to Read Today

    In today’s fast-paced world, it is more important than ever to cultivate a habit of continuous learning. Reading daily articles can be an effective way to stay informed, expand your knowledge, and keep up with the latest trends in your indu...

  4. The relationship between learning styles and academic

    In this study, we aimed to identify the learning styles of Turkish physiotherapy students and investigate the relationship between academic

  5. Evidence-Based Higher Education

    A recent study demonstrated that current research papers 'about' Learning Styles, in the higher education research literature

  6. A Review of Research on Learning Style

    ... research of cognitive style in the domain of Chinese teaching. Wang (2006) included the paper Research on Learners Learning Chinese as a Second Language and

  7. How Common Is Belief in the Learning Styles Neuromyth, and Does

    Education research is often published in journals that are outside the immediate field of education, but instead are linked to the subject being

  8. Learning Styles: A Review of Theory, Application, and Best Practices

    ... paper-based learning modules). Scores on assessment questions related ... Limited research correlating learning styles to learning outcomes

  9. An integrative debate on learning styles and the learning process

    Learning styles and teaching styles: A case study in foreign language classroom.

  10. The Learning Styles Educational Neuromyth: Lack of Agreement

    Moreover, students' intelligence was not found to drive teachers' assessment of their LS. This study adds to the body of evidence that is

  11. Full article: Learning Styles: Lack of Research-Based Evidence

    There is no empirical research that shows matching a student's preferred learning style to instruction produces better learning outcomes. In

  12. Learning Styles: Lack of Research-Based Evidence

    publish any research based on learning styles. Unfortunately, a search of academic databases of. scholarly, peer-reviewed journals recently

  13. Adaptive e-learning environment based on learning styles and its

    The purpose of this paper is to design an adaptive e-learning environment based on students' learning styles and study the impact of the

  14. Learning Styles: Concepts and Evidence

    The term “learning styles” refers to the concept that individuals differ in regard to what mode of instruction or study is most effective for them.