Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .
Enter the email address you signed up with and we'll email you a reset link.
- We're Hiring!
- Help Center
RESEARCH FOR PERSONALITY DEVELOPMENT
Research is purposeful activity of learning, Research is most essential and powerful tool for progress of Nation. Development in various spheres of life has taken place because of research, as it has increased quality of our life. Research. It is quest to answer unsolved questions by pushing back the hurdles of ignorance. It is based on courage and confidence and carried out with lot of patience. The importance of research is attested by various experts in all fields. Hence, research is dynamic, progressive and multidimensional concept in the modern world. Research has philosophical, sociological, psychological, technological and scientific bases. Research is learning activity, so it can be related to the domains of learning as cognitive, affective and psychomotor which leads to acquisition of knowledge, skills and the attitude, which forms the bases of personality development. Research Guide who supervise M.Phil., Ph.D. students can view research as a tool/technique along with the main purpose of research, for the development of their students personality.
Indu Bala Bala
Abstract:- Decision making is one of the core activities of education and is an essential element in any process to be executed. Decision making can be considered as a cognitive process that results in the selection of a certain belief or a course of action among some alternative possibilities. Every decision making process produces a final choice that may or may not inspire our actions. The present study is aimed at developing a tool on Decision Making Styles. After consultation with various experts in different fields of education, 44 items were selected initially from a draft of 50 items. Sample of 100 students were selected randomly for preliminary tryout from Sirsa district. In preliminary tryout 26 items were selected from 44 items. Second sample of 300 students were randomly selected for final tryout from the same population of Sirsa district. The main purpose for the development of this tool was to check the ability of students that how they make decisions about their career, educational decisions or decisions about their life.The Statistical Package for the Social Sciences (SPSS version 18) was employed for the purpose of data entry, manipulation and analysis. Validity and reliability of the items were also checked.
Dr. Siddhi Sood
Sarang S Bhola , Mr.Raju R. Shravasti
Present study is directed to know the relevance of working environment in service sectors like banking sector, insurance sector, education sector, hotel industry, tourism industry, communication etc. these service sectors are pure customers oriented and the organizations have to design policies in respect to conducive environment at workplace. It finds that there is positive correlation between working environment of the organizations and job satisfaction, Job involvement, employee’s productivity and efficiency of organization.etc. This research paper is based on secondary data in respect to conceptual as well as previous studies related to the significance of working environment in service sectors.
With the trend of innovative methods and giving due importance to subjective issues in educational research people started for looking the constructs of subjective termslike education, quality education, teacher effectiveness, teacher behaviour and the like. It is now being thought that dealing such problems with quantitative or qualitative research methods does not produce the acceptable results. Q-methodology is a combination of the two, thus can be termed as triangulation method. It combines the goods of both quantitative as well as qualitative methods of research. Paper explains theory as well as illustration of application of Q-methodology. At the same time it compares Q-methodology with R-methodology. It attempts to evaluate the utility of Qmethodology in solving subjective educational problems thus emphasizing its heuristic nature.
The future development of the country is in students hands. Because students and young people going to rule the country in future. Now a day's students are facing very high level academic stress. Every year about 25,000 students between 18-20 years commit suicides because of examination and other academic stress. It's time to understand the major academic stressors and how to manage these stressors. This study reveals the major academic stressors and how to manage the academic stress by using emotional intelligence. Emotional intelligence is the ability to identify emotions to evaluate and create them so as to support thought to understand emotions and emotional knowledge, to contemplatively regulate emotions, so as to promote intellectual and emotional growth. This study is based engineering students in south Tamilnadu, concentrated on 6 districts and researcher collected 510 samples from engineering college students. Structured questionnaire includes Students Academic Stress Scale (SASS) and Emotional Intelligence Scale (EIS). Study concludes that emotional intelligence is a key to managing academic stress and creating pleasant environment for the students and supports them to present their best.
Review Of Research Journal
Dr.Sukhwinder Singh Cheema
Abstract:-Education is the significant instrument of empowerment of individual, society and nation. Realizing this growth enhancing values of school education, governments led to universalize the school education which led to the involvement of public resources in the school education sector. This public funding policy includes direct public provision, subsidies to private suppliers or some mixture of both the methods in the form of New PPP Pattern. The developing world in general and Asian countries in specific education sector has been facing a resource crunch and has been victim of low level of priority in pre reform region. The problem got further aggravated with the adoption of the new economic policy league which paved the way for commercialization of education, and it has turned education from being social service to a marketable commodity. The post reform era has been evident of declining trends in public expenditure in education and especially school education. India, being a developing country, is not an exception to this scenario. The present scenario is one in which the demand of finance for education is growing at geometric ratio, but the government is facing severe budgetary crunch. Education is a concurrent subject and state funding accompanying with nationally sponsored schemes is a significant contributor to educational expenditure. Within the school education sector, the there are two major subsector: Primary and Secondary School education. The expenditure on education is classified under two heads: revenue expenditure (operational expenditure), and capital expenditure. Revenue expenditure comprises 95 per cent of total educational expenditure in Punjab. Punjab is one of the most developed states of the country, but it is not in the position to establish a lead in the country in terms of educational attainments. The analysis of the data of the public spending on education in the state is not in tune with its educational requirements, particularly in the context of weak educational outcomes and emerging challenges of human capital formation. It has been witnessed that Govt. of Punjab devote a relatively low share of the educational budget to elementary education, despite, it cover eight years of instruction and having good merit compared to the other levels of education. The effect of this squeeze of resources could be evident in the declining trends in allocations to elementary and secondary education after 1991. This situation shows that out of this inadequate budget, more than half goes to secondary education and the rest is share by other sectors of education with elementary education. The limited increase in education budget in real terms has in affects converted and reduced the education budget in to a salary budget. This pattern of financing of school education in the state has led to several social and economic consequences. Keywords: Financing, Budget, Revenue expenditure, Resources, Pattern, PPP.
Monthly Multidisciplinary Research Journal Review Of Research Journal
Dr. Ajay K Maske
Review Of Research
Joel Bezerra Lima , Fabrício Moraes de Almeida
Mohammad Amiri, PhD
Review of Research Journal, International Level Multidisciplinary Research Journal
Dr. SUDHA PINGLE
Flávio de São PEDRO FILHO
Ashish Kumar Katiyar
Review of research
Review of Research
Dr Zaffar Ahmad
Dr.Hem Raj , Fatma Gausiya
Dr. M . Mahendraprabu
Review of research journal
nagina S mali
Paban Ghosh , kabita lepcha
Dr. Hari Krishna Behera
Indian Streams Research Journal
kala yadav , Dr. SUDHA PINGLE
Golden Research Thoughts
MRIGANKA N DAS
Marcos Cesar Santos
Dr. Mithu A N J A L I Gayan
Dr Mamta Garg
Fabio Machado de Oliveira
Dr C Manikandan
Dr. Pushkar Dubey
Dr. Vikas Chaurasia
- We're Hiring!
- Help Center
- Find new research papers in:
- Health Sciences
- Earth Sciences
- Cognitive Science
- Computer Science
- Academia ©2023
- Reference Manager
- Simple TEXT file
People also looked at
Original research article, personality and developmental characteristics of primary school students ’ personality types.
- Department of Psychology, School of Philosophy and Sociology, Jilin University, Changchun, China
The aim of the current study was to investigate the personality characteristics and developmental characteristics of primary school students’ personality types in a cross-sectional sample of 10,366 Chinese children. The Personality Inventory for Primary School Student was used to evaluate primary school students’ personality. Latent profile analysis (LPA) was used to classify primary school students’ personality types. One-way ANOVA was used to explore the personality characteristics of personality types, and Chi-square tests were used to investigate grade and gender differences of primary school students’ personality types. Results showed that the primary school students could be divided into three personality types: the resilient, the overcontrolled, and the undercontrolled. Resilients had the highest scores, and undercontrollers had the lowest scores on all of five personality dimensions (intelligence, conscientiousness, extraversion, agreeableness, and emotional stability). The overcontrollers’ scores on personality were between the other two types, with lower emotional stability. As the grade level increased, the proportion of undercontrolled students in primary schools generally showed an upward trend and reached the maximum in grade 5. The proportion of resilient students in primary schools generally showed a downward trend. The proportion of resilient students was highest in grade 2 and lowest in grade 5. Girls were significantly more likely than boys to be resilient personality types, while boys were significantly more likely than girls to be undercontrolled personality types. The overcontrolled personality type did not show significant gender differences. Because of the undesirable internalizing problems related to overcontrollers and the externalizing problems related to undercontrollers, our results have implications for Chinese schools, families, and society in general.
Personality has significant impacts on many aspects of people’s everyday lives, such as interpersonal relationships, health, academic performance, and subjective wellbeing ( Neyer et al., 2014 ; Briki, 2018 ; Gray and Pinchot, 2018 ; Stajkovic et al., 2018 ). The primary school stage is an important developmental period for accumulating knowledge and learning to understand society. It is also an important stage for children’s personality development ( Soto and Tackett, 2015 ). Previous research has shown that personality development in primary school can effectively predict crime in adulthood ( Kachaeva et al., 2017 ). In studies of personality development, two main strategies a variable-centered and a person-centered approach are being distinguished ( Donnellan and Robins, 2010 ).
Variable-centered approaches are primarily reflected in studies on personality dimensions or traits, such as the dimensions of the five-factor model (FFM) of personality, the most widely employed model ( Bergman and Magnusson, 1997 ; John et al., 2008 ). The FFM yields five personality dimensions: agreeableness, extraversion, conscientiousness, emotional stability, and openness. Personality is the psychological properties with culture attribute ( Li and Zhang, 2006 ; Zhang, 2011 ). Based on Chinese culture and the characteristics of children’s personalities, Yang (2014) used teachers’ free description and vocabulary to collect and code personality trait adjectives for primary school students in China. They decided that the personality construction of Chinese primary school students was composed of five dimensions: extraversion, agreeableness, conscientiousness, emotional stability, and intelligence. Intelligence refers to the characteristics of individual self-awareness, intelligence, and talents, reflecting whether primary school students have their own independent ideas, high learning ability, and positive motivation in learning activities. Teachers’ assessment of intelligence was reflected in three aspects (intelligent, curiosity/creativity, and independent/enterprising). Western countries named the traits related to intelligence as openness. In addition to emphasizing the speed of brain reaction, it also emphasized appreciation of personal emotion, imagination, and the pursuit of a better life, beliefs, and values ( Carver and Scheier, 1996 ). In China, this dimension mainly reflects the speed of children’s brain reaction. This dimension also includes children’s independence and enterprising spirit ( Zhang, 2011 ). The dimension named as intelligence is more in line with Chinese educational philosophy. Cultural differences lead to differences in the connotations of personality traits involving intelligence.
Person-centered approaches study “types” identifying clusters of individuals with similar personality patterns ( Van Leeuwen et al., 2004 ). Person-centered approaches are concerned with how different dimensions are organized within the individual, which subsequently defines different types of person ( Herzberg and Roth, 2006 ). The typological approach emphasizes persons ( Hart et al., 2003 ). Personality types are intended specifically to represent the organization among traits that occurs within individuals ( Grumm and Collani, 2009 ). According to the results of several studies, the five personality traits in FFM can be combined with each other to form three personality types, resilient, undercontrolled, and overcontrolled ( Robins et al., 1996 ; Asendorpf and Van Aken, 1999 ; Asendorpf et al., 2001 ; Yang and Ma, 2014 ; Rosenström and Jokela, 2017 ). These three personality types have been repeatedly verified across different languages and cultures, different personality models, and different ages ( Donnellan and Robins, 2010 ; Chapman and Goldberg, 2011 ; Meeus et al., 2011 ; Alessandri et al., 2014 ; Leikas and Salmela-Aro, 2014 ; Specht et al., 2014 ; Yang and Ma, 2014 ). Previous research described the three personality types in terms of the FFM of personality description. Resilients were characterized by relatively high levels of openness, conscientiousness, extraversion, emotional stability, and agreeableness. Overcontrollers were characterized by low emotional stability, with moderate levels of the other four dimensions. Undercontrollers were characterized by relatively low levels of openness, conscientiousness, extraversion, emotional stability, and agreeableness ( Grumm and Collani, 2009 ; Zentner and Shiner, 2012 ; Yang and Ma, 2014 ; Chen, 2019 ; Zou et al., 2019 ). The resilient personality type is well adapted to society, verbally expressive, energetic, independent, self-confident, and able to adjust to situational demands using self-control. The overcontrolled personality type is socially maladaptive, emotionally brittle, interpersonally sensitive, tense, and inhibited prone to excessively restraining impulses. The undercontrolled personality type is socially maladaptive, impulsive, self-centered, manipulative, confrontational, disagreeable, and lacking in self-control ( Block and Block, 1980 ; Robins et al., 1996 ; Donnellan and Robins, 2010 ).
The identification of groups of persons is frequently done through cluster analysis (e.g., Magnusson and Bergman, 1990 ) or Q factor analysis (e.g., York and John, 1992 ). The goal of these analytic techniques is to maximize similarity among members of a group while minimizing resemblance of members of one group to members of all the others. These approaches have methodological limitations ( Klimstra et al., 2010 ). The number of types determined by these analytic techniques has the subjective judgment and theoretical orientation of the investigators. Moreover, the number of groups that is specified has implications for statistical analysis: If many groups are specified, then there may be few participants in each group, which makes parametric data analysis difficult. On the other hand, the formation of only a few groups may mean that participants have only limited resemblance to other participants in the same group, and consequently, viewing this group as representing a type of person can be misleading. There are no easy resolutions of this problem, which has led to calls for the development of new analytic techniques for person-centered research ( Singer and Ryff, 2001 ). LPA is an empirically driven method that defines taxonomies or classes of people based on common characteristics ( Lanza et al., 2003 ). LPA uses all observations of the continuous dependent variable to define these classes via maximum likelihood estimation ( Little and Rubin, 1987 ). Some studies point out that LPA is better than traditional Q factor analysis and cluster analysis ( Reinke et al., 2008 ). It not only eliminates the measurement errors in the construct, but also provides the researcher with more objective indices of fit to make up for the deficiencies of Q factor analysis and cluster analysis ( Bauer and Curran, 2004 ). Meeus et al. (2011) used LPA to divide adolescents into three personality types: the resilient, the overcontrolled, and the undercontrolled. This study found that as the age increased, the number of overcontrollers and undercontrollers decreased, whereas the number of resilients increased. Undercontrollers, in particular, were found to peak in early to middle adolescence. Asendorpf and van Aken (1999) classified the personality types of children from 4 to 10 years old, and found that more girls were rated as the resilient type than boys, and less girls were rated as the undercontrolled type than boys. However, in the context of Chinese culture, there are few studies on the personality characteristics and developmental characteristics of primary school students’ personality types.
Asendorpf and van Aken (1999) found that childhood personality types are good predictors of later development. Adjustment problems differ by personality type, which indicates the utility of conceptualizing students’ personalities in terms of types for both research and clinical practice ( Shiner and Caspi, 2003 ). Overcontrolled children may be especially at risk of developing internalizing problems (e.g., symptoms of depression and anxiety) due to their inhibited nature, whereas undercontrollers’ impulsivity may leave them vulnerable to the development of externalizing problems (e.g., aggression and attention problems; Yu et al., 2015 ; Achenbach et al., 2016 ; Bohane et al., 2017 ). Robins et al. (1996) found that undercontrollers had lower IQ scores, lower academic achievement at school, worse conduct, and more serious delinquency than overcontrollers and resilients. Focusing on personality types allows us to discern predictable patterns of risks to healthy development, helping teachers and parents educate children and intervene when necessary. Teachers and parents are more likely to prevent child maladaptive development based on developmental characteristics. Our research lays the foundation for psychologists to conduct future intervention research.
The current study aimed to investigate the personality characteristics of Chinese primary school students’ personality types and their developmental characteristics by grade and gender. Based on previous research, we hypothesized that (1) the primary school students would be divided into three personality types: the resilient, the overcontrolled, and the undercontrolled; (2) compared to the other two types, undercontrollers’ scores would be lower and resilients’ would be higher on all five personality dimensions (intelligence, conscientiousness, extraversion, agreeableness, and emotional stability), with the overcontrollers’ scores in between, with lower emotional stability; and (3) there would be significant grade and gender differences in primary school students’ personality types.
Materials and Methods
Participants and procedures.
We selected 21 primary schools in North China to issue the questionnaire. The questionnaire was used to evaluate primary school students by teachers. There were several classes for each grade and two teachers in each class. We took a multi-informant approach to reduce reporter bias in measurement of personality. Each student was assessed by two teachers; 636 teachers rated 10,366 primary school students (5,441 male and 4,925 female). There were 2,209 first graders, with an average age of 6.92 years old, 2,066 second graders, average age 8.06 years old, 2,218 third graders, average age 9.41 years old, 1,886 fourth graders, average age 10.03 years old, and 2,087 fifth graders, average age 11.24 years old. Written informed consent had been obtained from the parents’ guardians of all participants. All participants volunteered to join the experiments, and informed consents signed by their legal guardians.
Personality Inventory for Primary School Student
Teachers rated their students’ personality on the Personality Inventory for Primary School Student ( Zhang, 2011 ). The personality was measured using Zhang’s Chinese FFM, which was developed based on the original FFM. This personality inventory includes five dimensions, namely, extraversion, agreeableness, conscientiousness, emotional stability, and intelligence. This inventory includes 62 items. All items were rated on a five-point Likert scale from 1 (very inaccurate) to 5 (very accurate). Emotional stability is an inverted dimension. Items rated as 1 point are converted into 5 points, and items rated as 2 points are converted into 4 points. For example, when criticized by the teacher, the student will immediately become angry or frustrated. This item is a reverse scoring. After reverse scoring, the total score of each personality dimension is calculated. The higher the score, the higher the development level of the personality dimension. This questionnaire has good reliability and validity in the Chinese cultural context ( Zhang, 2011 ). In order to investigate the degree of consistency of the teacher’s evaluations, we had two teachers in each class fill out the personality rating scale on all primary school students in the class at the same time. The rater reliability and the Cronbach’s alphas of scale are presented in Table 1 . The consistency of the two raters’ evaluations of primary school students indicated that the evaluation results were objective and credible.
Table 1 . The rater reliability and the Cronbach’s alphas of the scale.
The SPSS 20.0 was used to conduct descriptive statistics and analysis of variance. Specifically, One-way ANOVA was used to explore the personality characteristics of personality types, and Chi-square tests were used to investigate the grade and gender differences of primary school students’ personality types. The Mplus 7.4 was used to conduct LPA. All data were treated with a statistical significance level of p < 0.05. LPA was executed to analyze primary school students’ personality types ( Muthén and Muthén, 2000 ). We chose the optimal model relying on the following criteria: Akaïke Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-Size Adjusted Bayesian Information Criterion (aBIC), Likelihood Ratio Tests (LMR), Bootstrap Likelihood Ratio Test (BLRT), and Entropy ( Nylund et al., 2007 ). Smaller values of AIC, BIC, and aBIC indicate better models ( Schwarz, 1978 ). The range of entropy is between 0.00 and 1.00. Higher values of entropy indicate higher the classification accuracy ( Hix-Small et al., 2004 ). For the LMR and BLRT, a value of p smaller than 0.05 suggests that the k class model is better than the k-1 class model ( Asparouhov and Muthén, 2012 ). If some types in a k class model already appear in a k-1 class model, the k-1 class model will be selected according to the principle of model simplicity ( Muthén and Muthén, 2000 ). In order to make the model more universal, Fisher and Robie (2019) pointed out that the large sample size should be randomly divided into two small samples, and LPA should be done separately. The SPSS 20.0 was used to randomly split a large sample size into two small subsamples. One subsample was used for exploratory LPA. Another subsample was used for cross-validation.
Personality Characteristics of Primary School Students’ Personality Types
A total of 10,366 participants were randomly divided into two samples. Sample 1 ( n 1 = 5,183) and sample 2 ( n 2 = 5,183) were used for LPA, respectively. The results showed that the potential category classifications of the two samples were similar (see Table 2 ). AIC, BIC, and adjusted BIC of the three-class model and four-class model were smaller than other models, the entropy values were all above 0.8, and the value of p for LMR and BLRT was both significant, which indicates that these two models fit well and the correct rate of personality type classification is higher than other models. Wang and Bi (2018) pointed out that the final model should be determined in conjunction with the actual meaning of classification. In the four-class model, the characteristics of the two types in the middle overlap and should be regarded as one type. In other words, the personality characteristics of these two types were very similar (see Figure 1 ). According to the principle of model simplicity, the three-class model should be the optimal model. In addition, the four-class model did not meet the condition that each type accounts for at least 5% of the total sample ( Nagin, 2005 ). The three-class model has clearer and more concise outlines, and the indicators also meet the criteria for suitability of LPA. LPA diagrams of the two samples are presented in Figures 2 , 3 . The results showed that the three-class model was the best model. It is reasonable to divide the personality of primary school students into three classes. This result is consistent with the number of types classified by Ma (2016) .
Table 2 . Latent profile analysis fitting index of personality dimension of primary school students ( n = 10,366).
Figure 1 . Potential profile of the four-class model ( n 1 = 5,183).
Figure 2 . Potential profile of the three-class model ( n 1 = 5183).
Figure 3 . Potential profile of the three-class model ( n 2 = 5,183).
One-way ANOVA and multiple comparisons were used to describe the characteristics of each personality type (see Table 3 ). The third personality type had the highest scores on all five dimensions (intelligence, conscientiousness, extraversion, emotional stability, and agreeableness). According to Robins et al. (1996) , we identified this type as the resilient personality type. The score of the second type on personality was between the other two types, with lower emotional stability. According to Zentner and Shiner (2012) , we identified this type as the overcontrolled personality type. The first personality type had the lowest scores on all five dimensions. According to Ma (2016) , we identified this type as the undercontrolled personality type.
Table 3 . Descriptive statistics, analysis of variance, and post-hoc tests on personality types in five dimensions.
Developmental Characteristics of Primary School Students’ Personality Types
In order to investigate the grade and gender differences in primary school students’ personality types, we first confirmed that the personality types were related to gender and grade (see Table 4 ). Results showed that personality types were indeed related to both grade ( χ 2 (8) = 217.016 ** , Φ = 0.102) and gender ( χ 2 (2) = 141.368 ** , Φ = 0.117). Since the interaction effects between grade and gender had no significant influence on personality type, we only examined the relationship between gender and personality type, and the relationship between grade and personality type. The second step was to examine the developmental characteristics of primary school students’ personality types in different grades. The population proportions presented in Figure 4 show the grade-related developmental trajectory of primary school students’ personality types. Chi-square tests were used to examine grade differences. Finally, a Chi-square test was conducted to examine gender differences.
Table 4 . The number and ratio of each type of personality in each grade and gender.
Figure 4 . Developmental trends of primary school students’ personality types.
Results showed that among the undercontrolled primary school students, there were significant differences between different grades ( χ 2 (4) = 99.413 ** , Φ = 0.098). The proportion of students in grade 1 was significantly lower than grade 2 ( χ 2 (1) = 22.091 ** , Φ = 0.072), grade 3 ( χ 2 (1) = 17.888 ** , Φ = 0.064), grade 4 ( χ 2 (1) = 34.738 ** , Φ = 0.092), and grade 5 ( χ 2 (1) = 96.101 ** , Φ = 0.150). There was no significant difference in the proportion between grade 2 and grade 3 ( χ 2 (1) = 0.241, Φ = 0.008). There was no significant difference in the proportion between grade 2 and grade 4 ( χ 2 (1) = 1.629, Φ = 0.020). The proportion of students in grade 2 was significantly lower than that in grade 5 ( χ 2 (1) = 25.400 ** , Φ = 0.078). The proportion of students in grade 3 was significantly lower than grade 4 ( χ 2 (1) = 3.113 △ , Φ = 0.028) and grade 5 ( χ 2 (1) = 30.953 ** , Φ = 0.086). The proportion of students in grade 4 was significantly lower than grade 5 ( χ 2 (1) = 13.290 ** , Φ = 0.058). This result showed that as the grade level increased, the proportion of undercontrolled students in primary schools generally showed an upward trend. Specifically, this type showed an upward trend from grade 1 to grade 2, a flat trend from grade 2 to grade 3, and an upward trend from grade 3 to grade 5. The proportion of boys in the undercontrolled primary school students was significantly higher than that of the girls ( χ 2 (1) = 108.128 ** , Φ = 0.102). Our results revealed a significant gender difference in the undercontrolled primary school students.
Among the overcontrolled primary school students, there were significant differences between different grades ( χ 2 (4) = 82.138 ** , Φ = 0.089). The proportion of students in grade 1 was significantly higher than grade 2 ( χ 2 (1) = 38.600 ** , Φ = 0.095), grade 4 ( χ 2 (1) = 8.321 * , Φ = 0.045), and grade 5 ( χ 2 (1) = 4.860 * , Φ = 0.034). The proportion of students in grade 1 was significantly lower than grade 3 ( χ 2 (1) = 5.960 * , Φ = 0.037). The proportion of students in grade 2 was significantly lower than grade 3 ( χ 2 (1) = 72.867 ** , Φ = 0.132), grade 4 ( χ 2 (1) = 9.870 * , Φ = 0.050), and grade 5 ( χ 2 (1) = 15.746 ** , Φ = 0.062). The proportion of students in grade 3 was significantly higher than grade 4 ( χ 2 (1) = 27.003 ** , Φ = 0.082) and grade 5 ( χ 2 (1) = 21.032 ** , Φ = 0.071). There was no significant difference in the proportion between grade 4 and grade 5 ( χ 2 (1) = 0.533, Φ = 0.012). This result showed that the proportion of overcontrolled students was highest in grade 3 and lowest in grade 2. There was no significant difference in the proportion of boys and girls ( χ 2 (1) = 0.907, Φ = 0.010).
Among the resilient primary school students, there were significant differences between different grades ( χ 2 (4) = 138.021 ** , Φ = 0.115). The proportion of students in grade 1 was significantly lower than grade 2 ( χ 2 (1) = 4.975 * , Φ = 0.034). The proportion of students in grade 1 was significantly higher than grade 3 ( χ 2 (1) = 46.181 ** , Φ = 0.103), grade 4 ( χ 2 (1) = 5.947 * , Φ = 0.038), and grade 5 ( χ 2 (1) = 55.306 ** , Φ = 0.114). The proportion of students in grade 2 was significantly higher than grade 3 ( χ 2 (1) = 78.852 ** , Φ = 0.137), grade 4 ( χ 2 (1) = 20.578 ** , Φ = 0.072), and grade 5 ( χ 2 (1) = 90.245 ** , Φ = 0.147). The proportion of students in grade 3 was significantly lower than grade 4 ( χ 2 (1) = 17.140 ** , Φ = 0.065). There was no significant difference in the proportion between grade 3 and grade 5 ( χ 2 (1) = 0.452, Φ = 0.010). The proportion of students in grade 4 was significantly higher than grade 5 ( χ 2 (1) = 22.820 ** , Φ = 0.076). This result showed that as the grade level increased, the proportion of resilient students in primary schools generally showed a downward trend. The proportion of resilient students was highest in grade 2 and lowest in grade 5. The result showed that the proportion of resilient primary school boys was significantly lower than that of the girls ( χ 2 (1) = 87.235 ** , Φ = 0.092). There was a significant gender difference in resilient primary school students.
Classification and Personality Characteristics of Students’ Personality Types
We divided the primary school students into three personality types using LPA. The types, resilient, overcontrolled, and undercontrolled, were consistent with previous research results ( Donnellan and Robins, 2010 ; Meeus et al., 2011 ; Rosenström and Jokela, 2017 ). We used five personality dimensions as outcome variables and type as grouping variable to do a one-way analysis of variance; resilients had the highest scores, undercontrollers had the lowest scores on all of the five personality dimensions, and overcontrollers’ scores were between the other two types, with lower emotional stability, consistent with previous research results ( Zentner and Shiner, 2012 ; Chen, 2019 ; Zou et al., 2019 ). The overcontrolled type represented the majority of the total sample. This finding was similar to the results of research by Ma (2016) , who also found that the overcontrolled group accounted for the majority in China. This may reflect the limitations of Chinese social norms ( Xie et al., 2016 ). Chang et al. (2011) considered students in China to be rule-abiding in their behavior patterns and encouraged to be compliant by Chinese teachers, which reflects the high requirements and expectations of self-control for students in China’s primary education system on the whole. This would explain why most Chinese students would fall in the overcontrolled group. The overcontrolled children were described by teachers as prosocial, well-liked by children and adults, and obedient and not as aggressive, self-assertive, and competitive. Resilients scored high on all five dimensions. The resilient children were described by self-confidence, independence, verbal fluency, and an ability to concentrate on tasks ( Robins et al., 1996 ). Chinese teachers do not have a positive attitude toward all these characteristics. Compared with independent thinkers, teachers actually prefer overcontrolled students. Therefore, the teacher’s assessment of students may have observer bias. In our study, we took a multi-informant approach to reduce reporter bias in measurement of personality. Undercontrollers scored low on all five dimensions. The undercontrolled children were described by impulsivity, disobedience, stubbornness, and physical activity ( Robins et al., 1996 ). Some of these characteristics might be considered advantages in the United States (e.g., being stubborn, physically active, uninhibited, and disobedient).
Developmental Characteristics of Students’ Personality Types
We found that as the grade level increased, the proportion of undercontrolled students in primary schools generally showed an upward trend. Specifically, this type showed an upward trend from grade 1 to grade 2, a flat trend from grade 2 to grade 3, and an upward trend from grade 3 to grade 5. The proportion of resilient students in primary schools generally showed a downward trend. The proportion of resilient students was highest in grade 2 and lowest in grade 5. In the whole primary school stage, overcontrolled students always made up the majority. Individual physical and mental development and the learning atmosphere change as children progress through the grades, and those changes are reflected in the gradual decrease in the scores of certain personality dimensions ( Galambos and Costigan, 2003 ). This is especially true for the non-adaptive undercontrolled primary school students. At the beginning of formal nine-year compulsory education, primary school students are in a transition period; they have not yet adapted to the changes in the learning environment, so their extraversion, openness, and emotional stability are on a downward trend ( Soto et al., 2011 ; Van den Akker et al., 2014 ). The results of our study imply that middle-grade pupils have basically adapted to school, and most of their personality dimensions reflect a period of steady development ( Yang et al., 2016 ). Therefore, the proportion of undercontrolled primary school students is relatively stable in the third and fourth grades. The proportion of undercontrolled primary school students increases rapidly after fourth grade, however. Entering the upper grades, primary school students not only face increased learning pressure, restrained creativity, reduced activity time, and decreased activity levels, but also physical and psychological changes. These changes are reflected in the declining trend of agreeableness, intelligence, extraversion, and conscientiousness ( Van den Akker et al., 2010 , 2014 ; Soto et al., 2011 ). This leads to an increase in the proportion of undercontrolled students and a decrease in the proportion of resilient students. At this age, resilient students are more likely to become undercontrolled or overcontrolled ( Chen, 2019 ).
The proportion of girls with the resilient personality type was significantly higher than that of boys; conversely, the proportion of boys with the undercontrolled personality type was significantly higher than that of girls. The overcontrolled personality type did not show significant gender differences. The gender difference in primary school students’ personality types is consistent with the results of previous studies ( Asendorpf and Van Aken, 1999 ). Gender differences in personality are either due to physical differences or due to gender socialization in childhood ( Van den Akker et al., 2014 ). Chinese social culture gives different behaviors and attitudes suitable for boys and girls. In the process of Chinese gender role socialization, girls are expected to show the gentleness, dignity, and virtue of a “lady” who adapts to the environment and exhibits self-control. Boys are expected to be “brave” and “fearless” and are encouraged to show impulsiveness, seek stimulus, and otherwise exhibit poor inhibition. Physiologically, girls secrete fewer male hormones than boys and then adopt more mature self-regulation methods when coping with stressful events. They usually show less impulsive and aggressive behaviors. Therefore, boys who cannot effectively restrain impulses and adapt to the environment are more inclined to become undercontrolled primary school students.
Primary school students could be divided into three personality types: the resilient, the overcontrolled, and the undercontrolled. Resilients had the highest scores, and undercontrollers had the lowest scores on all of five personality dimensions. The overcontrollers’ scores on personality were between the other two types, with lower emotional stability. As the grade level increased, the proportion of undercontrolled students in primary schools generally showed an upward trend and reached the maximum in grade 5. The proportion of resilient students in primary schools generally showed a downward trend. The proportion of resilient students was highest in grade 2 and lowest in grade 5. The proportion of girls with the resilient personality type was significantly higher than that of boys; conversely, the proportion of boys with the undercontrolled personality type was significantly higher than that of girls. The overcontrolled personality type did not show significant gender differences.
Data Availability Statement
The datasets for this manuscript are not publicly available because the topic is not finished. Requests to access the datasets should be directed to corresponding author.
The studies involving human participants were reviewed and approved by Jilin University Ethics Committee. Written informed consent to participate in this study was provided by the participants’ legal guardian.
YY designed the experiment, prepared the materials, and performed the experiment. YY and YZ analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.
This study was sponsored and funded by the Humanities and Social Science Foundation of China, State Education Ministry (Grant No. 20YJC190029).
Conflict of Interest
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.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Achenbach, T. M., Ivanova, M. Y., Rescorla, L. A., Turner, L. V., and Althoff, R. R. (2016). Internalizing/externalizing problems: review and recommendations for clinical and research applications. J. Am. Acad. Child Psy. 55, 647–656. doi: 10.1016/j.jaac.2016.05.012
PubMed Abstract | CrossRef Full Text | Google Scholar
Alessandri, G., Vecchione, M., Donnellan, B. M., Eisenberg, N., Caprara, G. V., and Cieciuch, J. (2014). On the cross-cultural replicability of the resilient, undercontrolled, and overcontrolled personality types. J. Pers. 82, 340–353. doi: 10.1111/jopy.12065
Asendorpf, J. B., Borkenau, P., Ostendorf, F., and Aken, M. A. G. V. (2001). Carving personality description at its joints: confirmation of three replicable personality prototypes for both children and adults. Eur. J. Pers. 15, 169–198. doi: 10.1002/per.408
CrossRef Full Text | Google Scholar
Asendorpf, J. B., and Van Aken, M. A. (1999). Resilient, overcontrolled, and undercontroleed personality prototypes in childhood: replicability, predictive power, and the trait-type issue. J. Pers. Soc. Psychol. 77, 815–832. doi: 10.1037/0022-3522.214.171.1245
Asparouhov, T., and Muthén, B. (2012). Using Mplus TECH11 and TECH14 to test the number of latent classes. Mplus Web Notes 14, 17–22.
Bauer, D. J., and Curran, P. J. (2004). The integration of continuous and discrete latent variable models: potential problems and promising opportunities. Psychol. Methods 9, 3–29. doi: 10.1037/1082-989X.9.1.3
Bergman, L. R., and Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Dev. Psychopathol. 9, 291–319. doi: 10.1017/S095457949700206X
Block, J. H., and Block, J. (1980). “The role of ego-control and ego-resiliency in the organization of behavior,” in Minnesota Symposium on Child Psychology. ed. W. A. Collins, Vol . 13 (Hillsdale, NJ: Erlbaum), 39–101.
Bohane, L., Maguire, N., and Richardson, T. (2017). Resilients, overcontrollers and undercontrollers: a systematic review of the utility of a personality typology method in understanding adult mental health problems. Clin. Psychol. Rev. 57, 75–92. doi: 10.1016/j.cpr.2017.07.005
Briki, W. (2018). Trait self-control: why people with a higher approach (avoidance) temperament can experience higher (lower) subjective wellbeing. Pers. Indiv. Differ. 120, 112–117. doi: 10.1016/j.paid.2017.08.039
Carver, C. S., and Scheier, M. F. (1996). Perspective on Personality. Boston: Allyn and Bacon.
Chang, L., Mak, M. C. K., Li, T., Wu, B. P., Chen, B. B., and Lu, H. J. (2011). Cultural adaptations to environmental variability an evolutionary account of east-west differences. Educ. Psychol. Rev. 23, 99–129. doi: 10.1007/s10648-010-9149-0
Chapman, B. P., and Goldberg, L. R. (2011). Replicability and 40-year predictive power of childhood ARC types. J. Clin. Child Adolesc. 101, 593–606. doi: 10.1037/a0024289
Chen, J. H. (2019). Personlaity Types in Junior High School Students:Development and Interpersonal Relationships’ Influence and Intervention-Based on Longitudinal Research Design. China: Liaoning Normal University.
Donnellan, M. B., and Robins, R. W. (2010). Resilient, overcontrolled, and undercontrolled personality types: issues and controversies. Soc. Personal. Psychol. Compass 4, 1070–1083. doi: 10.1111/j.1751-9004.2010.00313.x
Fisher, P. A., and Robie, C. (2019). A latent profile analysis of the five factor model of personality: a constructive replication and extension. Pers. Indiv. Differ. 139, 343–348. doi: 10.1016/j.paid.2018.12.002
Galambos, N. L., and Costigan, C. L. (2003). Emotional and Personality Development in Adolescence. United States: JE. A, Inc.
Gray, J. S., and Pinchot, J. J. (2018). Predicting health from self and partner personality. Pers. Indiv. Differ. 121, 48–51. doi: 10.1016/j.paid.2017.09.019
Grumm, M., and Collani, G. V. (2009). Personality types and self-reported aggressiveness. Pers. Indiv. Differ. 47, 845–850. doi: 10.1016/j.paid.2009.07.001
Hart, D., Atkins, R., and Fegley, S. (2003). Personality and development in childhood: a person-centered approach. Monogr. Soc. Res. Child 68, 1–109. doi: 10.1111/1540-5834.00231
Herzberg, P. Y., and Roth, M. (2006). Beyond resilients, undercontrollers, and overcontrollers? An extension of personality prototype research. Eur. J. Pers. 20, 5–28. doi: 10.1002/per.557
Hix-Small, H., Duncan, T. E., Duncan, S. C., and Okut, H. (2004). A multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use. J. Psychopathol. Behav. Assess. 26, 255–270. doi: 10.1023/B:JOBA.0000045341.56296.fa
John, O. P., Naumann, L. P., and Soto, C. J. (2008). “Paradigm shift to the integrative big-five trait taxonomy: history, measurement, and conceptual issues,” in Handbook of Personality: Theory and Research, Vol . 3. eds. O. P. John, R. W. Robins, and L. A. Pervin (New York: Guilford Press), 114–153.
Kachaeva, M., Shport, S., Nuckova, E., Afzaletdinova, D., and Satianova, L. (2017). A study of the impact of child and adolescent abuse on personality disorders in adult women. Eur. Psychiatry 41:S587. doi: 10.1016/j.eurpsy.2017.01.891
Klimstra, T. A., Hale, W. W. III, Raaijmakers, Q. A., Branje, S. J., and Meeus, W. H. (2010). A developmental typology of adolescent personality. Eur. J. Pers. 24, 309–323. doi: 10.1002/per.744
Lanza, S. T., Flaherty, B. P., and Collins, L. M. (2003). “Latent class and latent transition analysis,” in Handbook of Psychology: Research Methods in Psychology. eds. J. A. Schinka and W. A. Velicer (New York: Wiley), 663–685.
Leikas, S., and Salmela-Aro, K. (2014). Personality types during transition to young adulthood: how are they related to life situation and well-being? J. Adolesc. 37, 753–762. doi: 10.1016/j.adolescence.2014.01.003
Li, Y. H., and Zhang, J. X. (2006). A cross-cultural study on the personality of 7–10 years-old children between Greece and China. Chin. J. Clin. Psychol. 14, 331–333. doi: 10.1016/S0379-4172(06)60092-9
Little, R. J., and Rubin, D. B. (1987). Statistical Analysis With Missing Data. New York: Wiley.
Ma, S. C. (2016). Personality Characteristics of Children and Adolescents in China–National Norm Formulation, Equivalent Inter-Phase Development Characteristics and Personality Types. China: Liaoning Normal University.
Magnusson, D., and Bergman, L. R. (1990). “A pattern approach to the study of pathways from childhood to adulthood,” in Straight and Devious Pathways From Childhood to Adulthood. eds. L. N. Robins and M. Rutter (New York: Cambridge University Press).
Meeus, W., Van de Schoot, R., Klimstra, T., and Branje, S. (2011). Personality types in adolescence: change and stability and links with adjustment and relationships: a five-wave longitudinal study. Dev. Psychol. 47, 1181–1195. doi: 10.1037/a0023816
Muthén, B., and Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol. Clin. Exp. Res. 24, 882–891. doi: 10.1111/j.1530-0277.2000.tb02070.x
Nagin, D. S. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press.
Neyer, F. J., Mund, M., Zimmermann, J., and Wrzus, C. (2014). Personality-relationship transactions revisited. J. Pers. 82, 539–550. doi: 10.1111/jopy.12063
Nylund, K. L., Asparouhov, T., and Muth, N. B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct. Equ. Model. 14, 535–569. doi: 10.1080/10705510701575396
Reinke, W. M., Herman, K. C., Petras, H., and Ialongo, N. S. (2008). Empirically derived subtypes of child academic and behavior problems: co-occurrence and distal outcomes. J. Abnorm. Child Psychol. 36, 759–770. doi: 10.1007/s10802-007-9208-2
Robins, R. W., John, O. P., Caspi, A., Moffitt, T. E., and Stouthamer-Loeber, M. (1996). Resilient, overcontrolled, and undercontrolled boys: three replicable personality types. J. Pers. Soc. Psychol. 70, 157–171. doi: 10.1037/0022-35126.96.36.199
Rosenström, T., and Jokela, M. (2017). A parsimonious explanation of the resilient, undercontrolled, and overcontrolled personality types. Eur. J. Pers. 31, 658–668. doi: 10.1002/per.2117
Schwarz, G. (1978). Estimating the dimension of a model. Ann. Stat. 6, 461–464. doi: 10.1214/aos/1176344136
Shiner, R., and Caspi, A. (2003). Personality differences in childhood and adolescence: measurement, development, and consequences. J. Child Psychol. psyc. 44, 2–32. doi: 10.1111/1469-7610.00101
Singer, B. H., and Ryff, C. D. (2001). New Horizons in Health: An Integrative Approach. Washington, DC: National Research Council.
Soto, C. J., John, O. P., Gosling, S. D., and Potter, J. (2011). Age differences in personality traits from 10 to 65: big five domains and facets in a large cross-sectional sample. J. Pers. Soc. Psychol. 100, 330–348. doi: 10.1037/a0021717
Soto, C. J., and Tackett, J. L. (2015). Personality traits in childhood and adolescence: structure, development, and outcomes. Curr. Dir. Psychol. Sci. 24, 358–362. doi: 10.1177/0963721415589345
Specht, J., Luhmann, M., and Geiser, C. (2014). On the consistency of personality types across adulthood: latent profile analyses in two large-scale panel studies. J. Pers. Soc. Psychol. 107, 540–556. doi: 10.1037/a0036863
Stajkovic, A. D., Bandura, A., Locke, E. A., Lee, D., and Sergent, K. (2018). Test of three conceptual models of influence of the big five personality traits and self-efficacy on academic performance: a meta-analytic path-analysis. Pers. Indiv. Differ. 120, 238–245. doi: 10.1016/j.paid.2017.08.014
Van den Akker, A. L., Deković, M., Asscher, J., and Prinzie, P. (2014). Mean-level personality development across childhood and adolescence: a temporary defiance of the maturity principle and bidirectional associations with parenting. J. Pers. Soc. Psychol. 107, 736–750. doi: 10.1037/a0037248
Van den Akker, A. L., Deković, M., and Prinzie, P. (2010). Transitioning to adolescence: how changes in child personality and overreactive parenting predict adolescent adjustment problems. Dev. Psychopathol. 22, 151–163. doi: 10.1017/S0954579409990320
Van Leeuwen, K., De Fruyt, F., and Mervielde, I. (2004). A longitudinal study of the utility of the resilient, overcontrolled, and undercontrolled personality types as predictors of children’s and adolescents’ problem behaviour. Int. J. Behav. Dev. 28, 210–220. doi: 10.1080/01650250344000424
Wang, M. C., and Bi, X. Y. (2018). Latent Variable Modeling and Mplus Application. Chongqing, China: Chongqing Universit Press.
Xie, X., Chen, W., Lei, L., Xing, C., and Zhang, Y. (2016). The relationship between personality types and prosocial behavior and aggression in chinese adolescents. Pers. Indiv. Differ. 95, 56–61. doi: 10.1016/j.paid.2016.02.002
Yang, L. Z. (2014). Personality Development and Education of Children and Adolescents. Beijing, China: China Renmin University Press.
Yang, L. Z., and Ma, S. C. (2014). Junior middle school students’ personality types and their development characteristics. Psychol. Sci. 37, 1377–1384. doi: 10.16719/j.cnki.1671-6981.2014.06.016
Yang, L. Z., Ma, Z., Zhang, J. R., and Shen, Y. (2016). Group sequence tracking study on the personality development of children aged 6–12. Psychol. Sci. 38, 1123–1129. doi: 10.16719/j.cnki.1671-6981.20160516
York, K. L., and John, O. P. (1992). The four faces of eve: a typological analysis of women’s personality at midlife. J. Pers. Soc. Psychol. 63, 494–508. doi: 10.1037/0022-35188.8.131.524
Yu, R., Branje, S., Keijsers, L., and Meeus, W. (2015). Associations between young adult romantic relationship quality and problem behaviors: an examination of personality–environment interactions. J. Res. Pers. 57, 1–10. doi: 10.1016/j.jrp.2015.01.003
Zentner, M., and Shiner, R. L. (2012). Fifty Years of Progress in Temperament Research: A Synthesis of Major Themes, Findings, Challenges, and a Look Forward. United States: The Guilford Press, 673–700.
Zhang, J. R. (2011). A Follow-Up Study of the Structural Evaluation of Children’s Personality and Its Developmental Characteristics. China: Liaoning Normal University.
Zou, R., Xie, X. C., Li, J. J., Hong, X. B., and Fan, C. Y. (2019). Personality and life satisfaction in middle school students. Chin. J. School Health 40, 1506–1508. doi: 10.16835/j.cnki.1000-9817.2019.10.019
Keywords: primary school students, personality types, latent profile analysis, personality characteristic, developmental characteristic
Citation: Yu Y and Zhang Y (2021) Personality and Developmental Characteristics of Primary School Students ’ Personality Types. Front. Psychol . 12:693329. doi: 10.3389/fpsyg.2021.693329
Received: 10 April 2021; Accepted: 21 July 2021; Published: 18 August 2021.
Copyright © 2021 Yu and Zhang. 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: Yanyan Zhang, [email protected]
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.
- Account settings
- Advanced Search
- Journal List
- Eur J Investig Health Psychol Educ
Students’ Personality Contributes More to Academic Performance than Well-Being and Learning Approach—Implications for Sustainable Development and Education
1 Instituto de Psicologia e de Ciências da Educação [Institute of Psychology and Education], Universidade Lusíada-Norte, 4369-006 Porto, Portugal; tp.adaisulu.rop@obmopmp
2 Centro de Investigação em Psicologia para o Desenvolvimento (CIPD) [The Psychology for Positive Development Research Center], Universidade Lusíada-Norte, 4369-006 Porto, Portugal; moc.liamg@sardepanasus
The present study aimed to describe the predictive role of personality dimensions, learning approaches, and well-being in the academic performance of students. In total, 602 students participated in this cross-sectional study and completed a set of questionnaires assessing personality, learning approach, and well-being. Two indexes were calculated to assess affective and non-affective well-being. The results partially support the hypotheses formulated. Results revealed that personality temperament and character dimensions, deep learning approach, and affective well-being were significant predictors of academic performance. A deep approach to learning was a full and partial mediator of the relationship between personality and academic performance. The results improve the understanding of the differential contribution of personality, type of learning approach, and type of well-being to academic performance. Comprehending that personality is the strongest predictor of academic performance, after controlling the type of learning approach and the type of well-being, informs school policies and decision-makers that it is essential to encourage personality development in adolescents to improve academic performance. These results also have implications for educational policies and practices at various levels, including an emphasis on the role of well-being as an educational asset. Understanding the links between personality, well-being, and education is essential to conceptualize education as a vital societal resource for facing current and future challenges, such as sustainable development.
Academic performance results from interactions between several factors. In addition to the classic variables, such as intelligence (through capabilities) or socioeconomic level (through stimuli and opportunities), personality is a well-known predictor of academic performance. The role of personality of high school and university students for their academic performance is well-known [ 1 , 2 , 3 , 4 , 5 ] but its relationship with well-being is relatively unclear. According to Poropat [ 3 ], the relationship between personality and academic performance changes, especially between 11 to 16 years old, and well-being seems to decrease along with adolescence [ 6 , 7 ]. In addition, contemporary perspectives emphasize the importance of other variables, such as the type of approach to learning preferred by students, in understanding academic processes and results, as well as personality and well-being.
Academic performance is usually measured by the final grade obtained in the course, which is one of the most studied indicators of academic success. In view of the growing pressures for academic success, academic performance is of great importance for students, teachers, and the national education system responsible for the formulation and implementation of educational policies. Thus, there are theoretical reasons to believe that relatively stable individual traits (personality), a school and study-based variable (learning approach), and a more volatile individual variable (well-being), will be interrelated. Therefore, knowledge of these relationships will be useful not only to inform school policymakers, but also to inform educators and parents about which adolescents need more support, and in what areas, to achieve greater academic success.
Eccles and colleagues developed the Value-Expectancy Model, which is a relevant model for understanding achievement motivation, including academic achievement and academic performance. This model has been recently updated as a developmental, social cognitive and sociocultural perspective on motivation [ 8 ]. According to this model, students’ academic performance is strongly influenced by student achievement motivations, which are shaped by individual characteristics. Amongst the individual characteristics involved in the construction of achievement motivation, temperament and personality play important roles in students’ preferences for certain learning approaches. Although temperament and personality are relatively stable and dispositional dimensions, students’ motivations, including achievement motivations and academic performance, tend to be influenced by students’ emotional states, including well-being (both affective and non-affective well-being). Although research describing the influences of each one of these variables on academic performance is abundant, studies on the interaction between personality (including temperament), learning approaches, and well-being in predicting academic performance are scarce, and is the reason why this is the main objective of the present study.
1.1. Temperament and Character Dimensions of Personality
Sociocognitive models of personality are seen as an effort to model the structure of intra-individual personality and an attempt to explain personality by formulating conceptual models of the mental architecture underlying human experience and action patterns. These models consider the processes of knowledge construction as central to the human being and, therefore, must be central to the theoretical models of personality [ 9 , 10 , 11 ].
Cloninger and colleagues developed the Psychobiological Model of Personality that conceptualizes the personality as an organization of dynamic and nonlinear psychobiological processes [ 12 ], i.e., “the way a person learns to adapt to experience, or, more specifically, as the dynamic organization within the individual of the psychobiological systems by which a person both shapes and adapts uniquely to an ever-changing internal and external environment” ([ 13 ], p. 1). Thus, this model integrates genetic, neuro, and psychobiological aspects of the human personality into two dimensions: temperament and character. Temperament refers to innate individual differences, in associative responses to basic emotional stimuli that shape emotional habits and responses, measurable at the beginning of development, and reflected in brain structures and functions [ 12 ]. In addition, temperament refers to individual differences in associative conditioning and related human brain circuits [ 13 , 14 ]. In turn, character pertains to the self-regulating aspects of the personality, that is, the way a person shapes and adapts responses to external and internal conditions [ 12 ], including the executive, legislative, and judicial functions, necessary for the mental self-government and self-actualization of identity [ 15 ].
Four dimensions of temperament capture these individual differences: novelty seeking, harm avoidance, reward dependence, and persistence. Each extreme of temperament has advantages and disadvantages depending on the situation. Novelty seeking and harm avoidance are responsible for activating and inhibiting behaviors [ 12 ]; that is, novelty seeking is the tendency to respond to new stimuli, while harm avoidance is the tendency to inhibit behavior in the presence of aversive stimuli. In addition, novelty seeking reflects individual differences in the brain’s behavioral activation system, which is crucial for learning and for regulating motor habits and skills [ 12 ]. In turn, harm avoidance has an inhibitory inclination. It reflects the activity of the punishment system, a threat-processing device that anticipates, detects, and responds with defensive actions to hazards or threats [ 15 ]. The other two dimensions of temperament are responsible for maintaining behavior: reward dependence and persistence. Reward dependence is the tendency to respond positively and maintain behavior in the presence of signs of reward and social approval. Persistence, in turn, represents the tendency to persevere in long-term goals and maintain the behavior despite the frustration, fatigue, and lack of reward [ 12 ].
The three dimensions of character are self-directedness, cooperativeness, and self-transcendence. Self-directedness refers to an individual’s willpower or ability to control, regulate, and adapt his behavior to achieve relevant personal goals and values [ 12 ]. It also represents the individual’s ability to control his conduct and guide him towards personal goals and objectives, using his resources appropriately [ 16 ]. Cooperativeness refers to the empathic ability to accept others and identify their emotions and, if necessary, to forget personal gratifications for the benefit of the social group. Cooperativeness is related to an individual’s tolerance and acceptance, his ability to be sensitive to external needs, his tendency to help and manifest pro-social values, and to establish interpersonal exchanges [ 16 ]. Self-transcendence refers to how well individuals identify themselves as an integral part of the universe as a whole and their experience of something superior that goes beyond ourselves [ 12 ]. Individuals with high self-transcendence are prone to creativity, magical thoughts and religiosity.
Cloninger’s Psychobiological Personality Model was used in this study and not the Big Five Personality Model for three reasons. First, the Big Five Personality Model is a lexical model, the structure of which derives from linear data reduction, which does not provide a comprehensive conceptual explanation of how personality works [ 17 , 18 ]. For example, the Big Five Model does not distinguish qualitatively different processes, such as the emotional and cognitive components of the personality. Previous research has shown consistent and positive associations between neuroticism and the surface approach to learning, and between conscientiousness and the deep approach to learning [ 19 , 20 ]. Recent research has shown that neuroticism is a factor that comprises two qualitatively different psychobiological processes: high anxiety and low self-directedness. In turn, conscientiousness encompasses aspects of persistence and self-directedness [ 21 , 22 ]. In addition, in a recent study that examined the influences of Cloninger’s Psychobiological Personality Model on learning approaches, Moreira and colleagues [ 23 ] found that although students’ preferences for deep and surface learning approaches are best understood as integrated temperament-character profiles, temperament and character dimensions have independent significant effects on learning approaches. Finally, we chose to use Cloninger’s Psychobiological Personality Model, on the one hand because it conceptualizes temperament and character dimensions independently, and on the other hand because Eccles’ Expectation-Value Model, used in the framework of this study, explicitly refers to the emotional (temperament) and cognitive dimensions of the personality as distinct factors that exert an independent influence on performance motivation [ 8 ].
Adolescence is a period characterized by personality changes that influence developmental, emotional, social, and academic results. Although the personality is relatively stable, it is changeable and manifests itself differently at specific ages. Recently, Zohar et al. [ 24 ] found that temperament and character traits were only moderately stable from 12 to 16 years old. In particular, harm avoidance and persistence have decreased, while self-directedness and cooperativeness increased from 12 to 16 years old. The novelty seeking, reward dependence, and self-transcendence increased from 12 to 14 years and then decreased [ 24 ]. Therefore, during adolescence, personality dimensions can have substantially different influences, depending on the results we are evaluating.
1.2. Learning Approach
This construct refers to the relationship that students develop with learning tasks, a process that combines motivational guidance, and a type of learning strategy [ 25 , 26 , 27 ]. Thus, the learning approach refers to the understanding and meaning of the students’ learning experience, which is associated with personal (cognitive, affective, and interpersonal) and environmental factors (educational goals, content, methods, materials, resources) that influence and affect the academic processes and outcomes [ 28 ]. Knowing the type of approach students prefer and adopt, allows us to understand how students relate to learning tasks, in order to promote the understanding of individual variability at the study level [ 26 ]. Marton and Säljö [ 28 , 29 ] identified two contrasting approaches: a surface approach and a deep approach to learning [ 30 , 31 , 32 , 33 , 34 , 35 ].
The deep approach is characterized by the student’s underlying guiding intention to maximize intellectual understanding and extract meaning from the task, i.e., it presupposes the existence of intrinsic motivation. The student seeks to understand and establish relationships between concepts, generalize learning to new concepts, and different situations. Students who take this approach have an active interest in the themes and use logic to understand the concepts [ 25 , 26 ].
The surface approach is characterized by the existence of extrinsic task-oriented motivation and a superficial strategy. This approach is characterized by mechanical and reproductive learning, using the memorization of content, with low commitment and effort on the part of the student, with minimal time spent, but with anxiety to face demanding learning tasks. Surface motivation is considered instrumental, and the student’s goal is to learn the minimum necessary to fulfill what is required, pass the exam, and avoid failures [ 25 , 26 ].
Overall, existing studies suggest a significant and positive relationship between the deep learning approach and academic achievement [ 36 , 37 , 38 ]. Although there are several studies on the coexistence of the two learning approaches, as well as the prominence of each other, the results are inconsistent or have small effects and, therefore, cannot be generalized for all contexts [ 39 , 40 , 41 ]. This is mainly due to cultural, social, and contextual/learning environment factors.
Regarding personality and academic performance, based on the Big Five Model, studies show that agreeableness and openness to experience are positively associated with motivation for achievement, more effective involvement in educational experiences, and a deeper approach to learning [ 42 , 43 ]. Conscientiousness is associated with greater objective orientation [ 43 ] and extroversion with mastery, approximation, and performance objectives [ 32 ]. Neuroticism is associated with the avoidance of academic motivation (suggesting that students avoid aspects of academic life) and with a surface learning approach [ 27 ].
Recently, Moreira et al. [ 23 ], based on the Psychobiological Model of Personality, used a person-centered approach to assess the relationship between personality profiles and students’ preferred approach to learning. The authors found two profiles, one defined by less novelty seeking, greater reward dependence, and persistence that they labeled as “steady” profile, and the second was defined by greater novelty seeking, less reward dependence, and persistence, which was labeled as “disinhibit” profile. The results suggest that students with a “steady” temperament showed a preference for the deep approach to learning. Students with high character coherence also had this preference. A temperament profile-by-character profile interaction was crucial for understanding students’ preferred approach to learning, and implies that adaptive learning approaches result from an integration of the main learning and memory systems, as measured by the Junior Temperament and Character Inventory (JTCI).
1.3. Affective and Non-Affective Well-Being
The school is not just a place of excellence for learning. The school is also the place where adolescents can be happy and healthy, where they can make friends, develop social and emotional skills, and develop their personality. Thus, the school is a privileged place for the promotion of well-being, whether affective (associated with experiences of positive and negative situations and events) or non-affective (associated with the perception of social support, satisfaction, and quality of life) [ 44 , 45 ]. Affective well-being refers to the frequency and intensity of positive and negative emotions and mood. Non-affective or cognitive well-being refers to specific domains and global evaluations of life such as social support, quality of life and global life satisfaction. As a result, adolescent well-being is associated with several indicators of developmental trajectories [ 46 ], including school involvement [ 47 , 48 , 49 , 50 ] and academic achievement and performance [ 50 , 51 ]. Well-being is also a protective factor for negative health outcomes [ 52 ]. Adolescents with higher levels of well-being are more resilient [ 53 , 54 ], show less delinquency and aggressive behaviors, lower level of depression and anxiety symptoms, greater self-esteem, sense of effectiveness, and adaptation [ 53 , 54 , 55 ]. In addition, adolescents with high persistence and self-directedness showed higher well-being [ 56 , 57 ].
Thus, studies suggest that students who adopt a deep learning approach with mastery goals, greater involvement in self-regulated learning, and with the use of metacognitive skills, have a better academic performance. On the other hand, students who engage in academic tasks to demonstrate skills, reveal biased results of a negative pattern as they adopt surface learning strategies [ 58 , 59 ]. In addition, temperament and character dimensions are associated with learning, since learning is considered an organization of behavior as a result of individual experience [ 12 ]. In the study by Rosa and Moreira [ 60 ], the combination of certain personality dimensions (persistence and self-directedness) with certain learning approaches (surface and deep learning approach) explained 22% of the variance in academic performance. Interestingly, learning strategies also proved to be a significant mediator on the relationship between students’ interest in history and their achievement [ 61 ].
Therefore, this study aimed to know: (1) the dimensions of personality, (2) the type of learning approach, and (3) the type of well-being (affective and/or non-affective) contributing to academic performance, (4) which variable (personality, learning approach or well-being) contributes the most to academic performance, and, finally, (5) whether the type of learning approach plays a mediating role in the relationship between personality dimensions and academic performance.
Thus, considering the differential effects of personality, together with the role of the learning approach and the well-being of students, for academic performance, the following hypotheses were formulated:
personality dimensions are expected to contribute more to academic performance, regardless of the learning approach chosen by students;
well-being is expected to contribute more to academic performance, regardless of the learning approach chosen by students;
the deep learning approach is expected to be a mediator in the relationship between the dimensions of personality and well-being.
more well-being (affective and non-affective), a deep approach to learning, and more persistence, self-directedness, and cooperativeness, are expected to contribute positively to academic performance.
The results will allow to understand the role and contribution of these variables for students’ academic performance. These variables can be modified and promoted through emotional, social, and academic well-being programs implemented in the school context.
The study included 602 high school students (10th to 12th grade) from five schools in the north of the country, 346 female students (57.5%), 256 male students (42.5%) aged 14 to 17 years old (M = 16.07, SD = 0.8). Most of the students were attending regular education in scientific-humanistic courses ( n = 490, 81.4%), specifically in socio-economic sciences ( n = 32, 6.5%), social and human sciences ( n = 21, 4.9%), and science and technology ( n = 434, 88.6%). The remaining sample ( n = 112, 18.6%) were enrolled in vocational courses. The grade point averages (GPA) of the total sample (values for n = 593 students in the total sample) was 13.56 (SD = 1.96) on a scale of 0 to 20, ranging from 8 to 19 in this sample.
Students were invited to participate voluntarily in this study and were recruited from five schools in Northern Portugal, according to the snowball technique for the selection of nonrandomized samples. All students who collaborated presented their parents’ written informed consent and were gathered in a 1 h group session to complete the questionnaires in the presence of a member of the research team. The research protocol (Reference CIPD_Academic performance_20080) was approved by the Ethics Council on Behavioral Research of the Universidade Lusíada-Norte, Porto, and by the Directors of the schools where the data collection took place.
Sociodemographic Questionnaire: The sociodemographic characteristics of adolescents, such as age, gender, and grade were collected.
Academic performance: Grade point average (GPA) was collected on a scale from 0 to 20.
Learning Process Inventory, LPI [ 31 , 62 ]. This questionnaire consists of 19 items that evaluate the Deep Approach to Learning and 14 items that evaluate the Surface Approach to Learning. The higher the result, the greater the student motivation and learning strategies in a given learning approach. The Portuguese version [ 31 ] has good psychometric characteristics with an alpha of 0.83 for both approaches. The Cronbach’s alpha in this study for the Deep scale was 0.95 and for the Surface scale, it was 0.91.
Junior Temperament and Character Inventory, JTCI [ 12 , 63 ]. It consists of 127 items that measure the seven major dimensions of the Psychobiological Model of Temperament and Character. The dimensions and alphas were as follows for the four JTCI Temperament dimensions: Novelty Seeking (NS): 0.61; Harm Avoidance (HA): 0.50; Reward Dependence (RD): 0.32; Persistence (PS): 0.38. The three dimensions of Character and the respective alphas were as follows: Self-directedness (SD): 0.77; Cooperativeness (CO): 0.83; and Self-Transcendence (ST): 0.72. The Portuguese version [ 64 ] has good psychometric characteristics above (0.60) except in the dimension reward dependence (0.57).
KIDSCREEN-10 [ 65 ]. This 10-item scale assesses the quality of life in children/adolescents and higher results indicate greater satisfaction with the quality of life. The Portuguese version [ 66 ] has good psychometric characteristics (0.78). In this study, Cronbach’s alpha was 0.78.
Brief Student Life Satisfaction Scale, BSLSS [ 67 ]. This 6-item scale assesses satisfaction with life and higher results indicate greater satisfaction with life. The original version of this scale has an alpha of 0.75. This scale has already been used in a study with a similar sample [ 68 ] and, in this study, Cronbach’s alpha was 0.84.
Satisfaction Scale with Social Support for Children and Adolescents, SSSS [ 69 ]. This scale includes 12 items and higher results indicate greater satisfaction with social support. Portuguese version [ 69 ] has good psychometric characteristics (0.84). In this study, Cronbach’s alpha was 0.70.
Positive Affect and Negative Affect Scale, PANAS [ 70 , 71 ]. This scale includes 10 items that evaluate positive affect (PA) and 10 items that evaluate negative affect (NA) and higher results indicate higher PA and NA. The Portuguese version [ 71 ] has good psychometric characteristics (PA 0.86; NA 0.89). Cronbach’s alphas in this study were as follows: 0.90 for PA and 0.92 for NA.
Composite Non-affective Index and Affective Index. We estimate the two indices as indicators of non-affective and affective well-being, respectively. We follow the suggestion of Cloninger and Zohar [ 44 ] and Josefsson et al. [ 45 ] for this estimate. The Non-Affective Index (non-affective well-being) refers to the average of satisfaction with social support, life satisfaction, and health-related quality of life. The Affective Index (affective well-being) was estimated by the positive affect score minus the negative affect score; it thus reflects the emotional tone of the individual’s experience: the salience of positive emotions (desirably present) and negative emotions (desirable absence). These indices were already used in similar samples [ 68 ].
2.4. Statistical Analysis
This is a cross-sectional study. The sample characteristics were analyzed using descriptive statistics. Pearson’s coefficients were calculated to examine the relationship between the study variables. The dependent variable (academic performance) was filled in by 593 students. Missing values were not replaced, taking into account the type of qualitative variable we are dealing with, so only 593 participants were included in the analysis and not 602. To assess the degree to which personality dimensions, type of approach to learning, affective and non-affective well-being differentially contribute to academic performance, controlling for the type of course (regular versus vocational), a hierarchical multiple linear regression model was tested. The hierarchical regression model was performed within four steps evaluating whether personality dimensions (2nd step), the type of learning approach (3rd step) and, the type of well-being (4th step), contribute to academic performance, controlling for the type of course (1st step). This model also evaluated how much additional variance of academic performance is explained by each of these variables/steps. All the scales of JTCI were included in the regression models regardless of their significant relationship with the dependent variable (academic performance). The premises for conducting Multiple Linear Regression were met, namely, linearity, homogeneity of variances and multicollinearity (such as Variance Inflation Factor-VIF) scores below 10 and tolerance scores above 0.2). Mediation analyses to test the role of learning approach as a mediator between personality and academic performance were carried out using the PROCESS macro for SPSS. All the analyses were performed with Software IBM ® SPSS ® , version 25.0. A significant level of p -value ≤ 0.05 was assumed.
3.1. Relationships between Personality, Learning Approaches, Affective and Non-Affective Well-Being, and Academic Performance
Table 1 shows the relationships between personality, learning approaches, affective and non-affective well-being, and academic performance.
Relationships between personality, type of learning approach, well-being, and academic performance ( n = 593).
Note. Type of course and gender were coded as a dummy variable with vocational courses = 0 and regular courses = 1; boys = 0 and girls = 1; ** p < 0.01, * p < 0.05.
3.2. Hierarchical Multiple Regression Testing Personality, Deep and Surface Approaches to Learning and Well-Being, as Predictors of Students’ Academic Performance
The hierarchical multiple regression model ( Table 2 ), tested the variance of academic performance explained by personality dimensions, type of learning approach, and well-being, controlling for the type of course. The first step controlled for the type of course in which students were enrolled explaining 1% of the variance. The second step included personality dimensions and explained 11% of the variance of academic performance. The third step included learning approaches and the model explained 14% of the variance. The fourth step added affective and non-affective well-being and the model explained 15% of the variance of academic performance The final model explained 15% of the variance, F(12,580) = 8.477, p < 0.001. Personality dimensions added 10% of variance to the model, the type of learning approach added 3% and well-being added 1% of variance to the model.
The summary output of hierarchical multiple regression model testing personality, type of approach to learning, and well-being as predictors of students’ academic performance.
Note. Course type was coded as a dummy variable with vocational courses = 0 and regular courses = 1; *** p < 0.001, ** p < 0.01, * p < 0.05.
3.3. Approach to Learning as a Mediator between Personality and Academic Performance
Personality dimensions were associated with academic performance but, in order to increase knowledge about the mechanism by which they influence academic performance, a set of mediation analyses were carried out to explore the role of the type of learning approach as a mediator in this relationship. The deep learning approach proved to be a partial mediator in the relationship between persistence and academic performance, suggesting that the positive relationship between persistence and academic performance is partially mediated by the deep learning approach, F(2.590) = 23.36, p < 0.001, explaining 27% of the variance. The indirect effect was B = 0.270 BootSE = 0.077 (0.120 0.426) ( Figure 1 ).
Partial mediation of deep approach to learning between persistence and academic performance.
The deep learning approach was a partial mediator in the relationship between self-directedness and academic performance, suggesting that the positive relationship between self-directedness and academic performance is partially mediated by the deep learning approach, F(2.590) = 21.90, p < 0.001, explaining 26% of the variance. The indirect effect was B = 0.206 BootSE = 0.059 (0.986 0.328) ( Figure 2 ).
Partial mediation of deep approach to learning between self-directedness and academic performance.
The deep learning approach was a total mediator in the relationship between novelty seeking and academic performance, suggesting that the negative relationship between novelty seeking and academic performance is mediated by the deep learning approach, F(2.590) = 15.32, p < 0.001, explaining 22% of the variance. The indirect effect was B = −0.190 BootSE = 0.057 (−0.309 −0.091) ( Figure 3 ).
Total mediation of deep approach to learning between novelty seeking and academic performance.
The deep learning approach was a total mediator in the relationship between cooperativeness and academic performance, suggesting that the positive relationship between cooperativeness and academic performance is mediated by the type of deep learning approach, F(2.590) = 15.57, p < 0.001, explaining 22% of the variance. The indirect effect was B = 0.151 BootSE = 0.049 (0.065 0.257) ( Figure 4 ).
Total mediation of deep approach between cooperativeness and academic performance.
The surface approach to learning was not a mediator between personality and academic performance.
This study aimed to understand the differential contribution of personality, the type of approach to learning, and the type of well-being for academic performance, as well as the mediating role of the type of approach to learning. The results showed that novelty seeking (high), harm avoidance (high), persistence (high), self-directedness (high), and self-transcendence (low) were the personality dimensions that contributed to academic performance [ 37 , 38 , 63 , 72 ]. The deep approach to learning has proven to be a significant predictor of academic performance and a significant mediator of the relationship between personality (novelty seeking, persistence, cooperativeness, and self-directedness) and academic performance. Affective well-being was a significant negative predictor of academic performance, unlike non-affective well-being, which was not a significant predictor. These results were significant controlling the type of course in which students were enrolled (students on vocational courses) and the variation explained in academic performance was residual (1%).
According to the Psychobiological Model of Personality [ 12 ], individuals high in novelty seeking are impulsive, curious, and enthusiastic, easily engaging in new ideas, activities, and tasks. For these individuals, everything is a challenge, and they are described as people who “hunger for knowledge” ([ 73 ], p. 842). Therefore, this is a personality trait that facilitates learning.
Individuals high in harm avoidance are pessimistic, fearful, worried, and frightened; they avoid novel stimuli and show a slow adaptation to new situations [ 12 ]. However, one of the advantages of high levels of harm avoidance is the greater care and caution with which they anticipate possible hazards, and carefully plan tasks and activities. As academic achievement is associated with a high inhibitory control (measured by high harm avoidance) [ 23 ], they may need more reinforcement and attention from teachers.
Individuals high in persistence are hardworking, persistent, stable, effortful, ambitious, responsible, and perfectionist workers, despite frustration and fatigue (which are perceived as a challenge). Besides, highly persistent individuals tend to set more challenging goals and commit to pre-defined tasks when compared to individuals with low levels of persistence. This personality trait is in accordance with the principles of a deep approach to learning, characterized by an intrinsic motivation to maximize intellectual understanding and extract meaning from the task [ 27 , 28 , 29 , 30 ].
Self-directedness refers to the individual’s ability to control and guide his conduct towards personal goals and objectives, using his resources appropriately [ 12 , 16 ]. Directional individuals are mature, strong, self-reliant, responsible, goal-oriented, constructive, effective, and able to adapt their behavior to personal choices and voluntary goals. Also, this dimension is associated with good self-esteem and a history of consistent bonding.
Individuals with low self-transcendence have low spirituality and little awareness of being part of a holistic reality that transcends their own individuality [ 12 ]. For this reason, students with low self-transcendence have a profile characterized by an organized and practical but not creative structure; they are materialistic, task-focused, and socially adapted, thus, achieving good academic performance.
Thus, individuals with high novelty seeking, harm avoidance, persistence, and self-directedness, but with low self-transcendence, showed better academic performance. The results are in line with previous studies [ 31 , 36 , 37 , 40 , 51 ]. Of the set of variables included in the model, personality was the one that explained the greatest variance in academic performance (11%), confirming hypothesis 1. The type of approach to learning and well-being explained a residual variance, emphasizing the role of personality in academic performance when approached in conjunction with the type of learning and well-being. Hypothesis 2 was not confirmed.
To explore the mechanism by which personality influence academic performance, we tested the role of learning approach as a mediator, and the results showed that a deep approach was a significant mediator of academic performance, as other studies have also highlighted, but not the surface approach [ 37 , 38 ]. Thus, our results add knowledge about the total mediation effect of deep approach in the relationship between persistence and self-directedness, and academic performance, which suggests that this type of approach to learning has a unique and independent positive effect on academic performance in addition to the explained effect by personality. The deep approach was a total mediator in the relationship between cooperativeness and novelty seeking, and academic performance. The results showed that the negative relationship between novelty seeking and academic performance no longer exists in the presence of a deep approach to learning, emphasizing the unique and independent positive effect of the deep approach on academic performance, in addition to the effect explained by cooperativeness. These results confirmed hypothesis 3 and are in accordance with the results of Yongjun and Reese’s [ 61 ] study.
Interestingly, affective well-being was a significant negative predictor of academic performance, while non-affective well-being was not a significant predictor, unlike the results found in other studies [ 50 , 74 ]. However, a recent meta-analysis of the relationship between academic performance and subjective well-being concluded that students with low performance do not necessarily report low well-being and that high-performance students do not automatically show high well-being [ 75 ], that is, a relatively small effect was found for the relationship of well-being and academic performance. The same was found in our study: academic performance and well-being were statistically significant but only relatively weak. We believe that students with higher academic performance are also more anxious, dedicated, and focused and, therefore, dedicate themselves more to studies, obtaining better grades, but sacrificing their well-being.
As hypothesized personality traits influence the relationship between well-being and academic performance. Novelty seeking is a personality trait that facilitates learning because it activates behavior [ 12 , 73 ], but harm avoidance inhibits behavior, and adolescents with high levels of this trait are pessimistic, fearful, concerned, and frightened [ 12 ]. As academic performance is associated with a high inhibitory control (measured by high harm avoidance) [ 31 ], these adolescents may need more reinforcement, attention and support from teachers. In addition, highly persistent adolescents tend to set more challenging goals, are hardworking, and, due to high levels of self-directedness, are more self-reliant, responsible, and constructive [ 12 , 16 ]. Finally, adolescents with low self-transcendence need to have control over everything: they are materialistic and very focused (even too much) on studies/work, seem dissatisfied with what they have in life, do not establish strong relationships with nature and people and, therefore, may have low well-being, as found in this study. Thus, hypothesis 4 was partially confirmed.
4.1. Limitations and Directions for Future Research
This study has some limitations that should be acknowledged, such as the cross-sectional design, which prevents us from establishing causal relationships, and the sample collection occurred in the North of the country, making it difficult to generalize the results to the whole country. Three alpha values found in this study are considered low (HA, RD, and PS dimensions). The use of measures to correct these alpha values was not considered because of the empirical validation that the model has been receiving both in different populations, ages, and functioning domains. On the one hand, the Psychobiological Model of Personality is a model that is characterized by having a complex factorial structure, which tends to have implications for the factor pattern matrix [ 76 ], often reflecting on the reliability of the scale. On the other hand, the same instrument presented higher and acceptable reliability values in previous studies in the current population [ 23 , 68 ]. However, and in spite of the robust empirical validation of the model, results of our study need to be considered with some caution, and future studies need to overcome this limitation.
Economic or social indicators, such as income or family size, were not controlled in this study and, therefore, future studies should control these variables. In addition, the variables addressed in this study focused exclusively on the student. Bearing in mind the importance of the discipline-teaching process interaction in determining the types of learning approaches, future studies should include variables that focus on students but also on the context [ 77 ].
4.2. Study Strengths
Although research describing the influences of learning and personality approaches on academic performance is abundant, studies on the interaction between personality (including temperament), learning approaches, and well-being in predicting academic performance, are scarce. Previous research has already shown consistent and positive associations between neuroticism and surface approach, and between conscientiousness and deep approach [ 19 , 20 ]. Recently, using Cloninger’s Psychobiological Model of Personality, Moreira et al. [ 23 ] found that, although students’ preferences for deep and surface learning approaches are best understood as integrated character and temperament profiles, temperament and character dimensions when analyzed separately, are associated with different learning approaches. Thus, this study adds to the existing literature and knowledge about the contribution of personality dimensions above and beyond the learning approaches and well-being, in a sample of high school adolescents.
4.3. Implications for Practice
Taking into account the variance in academic performance explained by personality, programs that nurture personality should be developed and implemented in schools. Despite the contribution of the deep approach to learning and the fragile relationship with affective well-being, academic performance seems to be more dependent on personality characteristics. Therefore, school-based policies and practices to promote academic performance should include activities and programs that promote a healthy personality development [ 78 ]. In addition, it is important to understand the students’ preferred approach to learning because the way students approach a learning task strongly influences the quality of learning outcomes [ 79 ]. Moreover, a deep approach to learning was a mediator between personality and academic performance, emphasizing that there are relationships that occur only in students who adopt a deep approach to learning. Besides the fact that personality predicts learning approaches [ 23 ], a recent study demonstrates that different combinations of temperament and character profiles significantly predict different dimensions of student engagement with school [ 80 ]. Together with these results, our study supports the need for schools to assume their responsibility in promoting students positive holistic development, by the systematic promotion of personality development and well-being [ 81 ].
Thus, well-being must be considered a fundamental educational asset in the conceptualization of education as a vital resource of society to face current and future challenges, such as sustainable development. A new paradigm in education is required for schools to be more efficient in preparing students to deal with the challenges that humanity faces, such as the need to promote sustainable behaviors [ 82 , 83 ]. “Personality development is a core dimension of holistic development and the most promising pathway for societies to promote youths holistic development is to move to person-centered schools” ([ 84 ], p. 183).
The results improve the understanding of the differential contribution of personality, type of learning approach, and type of well-being to academic performance. Understanding that personality is the strongest predictor of academic performance, after controlling the type of learning approach and the type of well-being, informs school policies and decision-makers that it is essential to encourage personality development in adolescents to improve academic performance.
P.M.: Conceptualization, methodology, writing-review and editing, visualization, supervision, project administration, funding acquisition; S.P.: Formal analysis, writing-original draft preparation, visualization; P.P.: Investigation, data curation, visualization. All authors have read and agreed to the published version of the manuscript.
Support for this research comes from national funds from the Fundação para a Ciência e Tecnologia I.P. (FCT) [Portuguese Foundation for Science and Technology], under the Projects PTDC/MHC-CED/2224/2014; CIPD-BI-UID/PSI/04375/2016, and PTDC/CED-EDG/31615/2017, and by the Minerva Foundation–Ensino, Cultura e Investigação Científica (the founding organization of the Lusíada Universities).
Conflicts of Interest
The authors declare that they have no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.