self efficacy research papers pdf

• DOI: 10.1037//0033-295X.84.2.191
• Corpus ID: 7742072

Self-efficacy: toward a unifying theory of behavioral change.

• Published in Psychology Review 27 February 1977

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Cognitive processes mediating behavioral change., march 1977 number 3 cognitive processes mediating behavioral change, on the facilitation of self-efficacy, self-efficacy mechanism in human agency., tests of the generality of self-efficacy theory, theoretical and methodological difficulties in bandura's self-efficacy theory.

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Principles of behavior modification

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ORIGINAL RESEARCH article

Resilience building in students: the role of academic self-efficacy.

• Psychology and Public Health, University of Salford, Salford, UK

Self-efficacy relates to an individual's perception of their capabilities. It has a clear self-evaluative dimension leading to high or low perceived self-efficacy. Individual differences in perceived self-efficacy have been shown to be better predictors of performance than previous achievement or ability and seem particularly important when individuals face adversity. The study investigated the nature of the association between academic self-efficacy (ASE) and academic resilience. Undergraduate student participants ( N = 435) were exposed to an adverse situation case vignette describing either personal or vicarious academic adversity. ASE was measured pre-exposure and academic resilience was measured post-exposure. ASE was correlated with, and a significant predictor of, academic resilience and students exhibited greater academic resilience when responding to vicarious adversity compared to personal adversity. Identifying constructs that are related to resilience and establishing the precise nature of how such constructs influence academic resilience will assist the development of interventions aimed at promoting resilience in students.

Introduction

Psychological resilience.

A shift in emphasis in mental health policy to include promotion of positive mental health as a preventative measure ( WHO, 2005 ), together with the identification of resilience and coping as one of eight positive mental health grouping ( Parkinson, 2008 ), underlines the value of studies examining resilience. Abiola and Udofia (2011) suggest resilience is associated with increased quality of life, wellbeing and functional capacity in times of adversity. Although there is an intuitive appreciation for the “meaning” of resilience and what it infers (about the individual), consensus in defining psychological resilience, both conceptually and operationally as a measurable construct, has yet to be reached. As Friedland (2005) notes, perspectives on resilience are highly diverse and the concept of resilience is highly elusive. In an attempt to illustrate the concept, Gilligan (2001) uses the example of a resilient child as a child who does better than they ought to, bearing in mind what has happened to them. Friedland (2005) goes on to discuss resilience as inferring hardiness, toughness, and resistance, along with—somewhat paradoxically—elasticity and flexibility. This suggests that resilience is both multi-faceted and multi-leveled and the range of available definitions reflects this in both their depth and their breadth. Resilience is described by Hamill (2003) as competence in the face of adversity and by Gilligan ( 2001 , p. 5) as “a set of qualities that help a person to withstand many of the negative effects of adversity.” Pooley and Cohen ( 2010 , p. 34) define resilience as “the potential to exhibit resourcefulness by using internal and external resources in response to different contextual and developmental challenges….” Garmezy and Masten ( 1991 , p. 459) refer to resilience as “the process of, capacity for, or outcome of successful adaptation despite challenging circumstances.” Abiola and Udofia (2011) offer a fuller account, discussing resilience in terms of inner strength, competence, optimism, flexibility, and the ability to cope effectively when faced with adversity, minimizing the impact of risk factors, such as stressful life events, and enhancing the protective factors, such as optimism, social support, and active coping, that increase people's ability to deal with life's challenges.

Although seemingly diverse, most definitions of resilience feature adaptive, resourceful and innovative enabling responses to adversity, threat or challenge as a core element. As such, resilience is considered an asset or strength, a desirable and advantageous quality, characteristic or process that is likely to impact positively on aspects of an individual's performance, achievement, health, and wellbeing ( Bartley et al., 2010) .

As is common with many psychological constructs—self-efficacy for example ( Bandura, 1997 )—, there is debate around the existence and relevance of a global resilience construct. Instead, there is a strong argument for resilience to be considered—and measured—as a context-specific construct. Riley and Masten (2005) explain the need to contextualize resilience on the basis that judgments about risk and adversity relate directly to events and contexts, as do evaluations of competencies and outcomes. Both Liddle (1994) and Waxman et al. (2003) refer to the need to contextualize resilience in order to generalize findings from resilience studies and in order to consider specific practical implications for building resilience. The present study examines resilience in the context of education and learning (i.e., academic resilience), considering resilience as an asset and seeking to identify factors that may contribute to resilience promoting interventions for students, suggested by Zautra (2009) to have long-term benefits.

Academic Resilience

Wang et al. (1994) refer to academic resilience as an increased likelihood of (academic) success despite environmental adversities. Resilient students are described by Alva (1991) as those who maintain high motivational achievement and performance even when faced with stressful events and conditions that place them at risk of poor performance and by Waxman et al. (2003) as those who succeed at school despite the presence of adverse conditions.

As is the case with general resilience, work focussing on academic resilience has led to the emergence of apparently distinct yet related concepts and constructs, each aiming to address a seemingly similar issue. Although drawing some explicit distinctions between their own constructs and resilience ( Perkins-Gough, 2013 ), both Duckworth and Dweck provide significant contributions to the field of academic resilience with their work on “grit” and “mindset.” Duckworth describes grit as an individual's tendency to sustain interest, passion, effort and persistence toward achieving long-term future goals (despite challenges and failures) and reports grit as a better predictor of academic success than IQ ( Duckworth et al., 2007 ; Duckworth, 2013 ) or talent ( Duckworth and Quinn, 2009 ). Dweck's (2006 , 2010 ) work on mindset has led to the identification of two types of mindset, fixed and growth. A fixed mindset describes individuals with fixed beliefs regarding their level of intelligence and ability, which they believe remain stable. A growth mindset instead describes individuals who view their intelligence and ability simply as a basis for development and believe that challenges, including failure, are opportunities to develop their capacity for success through effort and practice. The influence of mindset is emphasized further by Snipes et al. (2012) , who consider a growth mindset to be a major contributory factor in the development of grit. Despite noted dissimilarities—Duckworth considers resilience to be only one factor explaining grit ( Perkins-Gough, 2013 )—there are clear overlaps between academic resilience and the constructs proposed by Duckworth and Dweck, and their relevance is illustrated by Farrington et al. (2012) who reports that the combination of a growth mindset and grit in students is been associated with higher academic grades.

Another construct, closely related to academic resilience, proposed by Martin and Marsh (2008 , 2009) is academic buoyancy. Described as the “capacity to overcome setbacks, challenges, and difficulties that are part of everyday academic life.” ( Martin, 2013 , p. 488) it is seen as distinct from academic resilience, which instead relates to the capacity to overcome significant adversity that threatens a student's educational development. Martin (2013) does present evidence that whilst buoyancy and resilience are related, buoyancy better predicts low-level negative outcomes and resilience better predicts major negative outcomes, which aligns with Martin and Marsh's (2008) earlier description of buoyancy as reflecting “everyday” academic resilience.

Waxman et al. (2003) suggest that studying resilient students will provide important implications for improving the education of students at risk of academic failure and evidence already exists supporting the relevance of academic resilience. McLafferty et al. (2012) reported that both resilience and emotional intelligence predicted coping at university, with resilience as the only significant unique predictor of coping subscales for grades, attendance, and studying. Furthermore, Abiola and Udofia (2011) reported higher perceived stress, anxiety and depression in low resilience medical students following completion of a major professional examination.

Waxman et al. (2003) note that resiliency refers to factors and processes that limit negative behaviors associated with stress and result in adaptive outcomes in the presence of adversity. They discuss the value of resilience studies that identify differences between resilient and non-resilient students and that focus on alterable factors to design more effective educational interventions. They suggest that focusing on educational resilience and those factors that can be altered to promote resilience may help address the gap in achievement between those students who are successful and those who are at risk of failure. Like Wagnild (2009) , Waxman et al. (2003) further suggest that rather than being fixed, academic resilience can be promoted by focussing on alterable factors including social competence, problem-solving skills, autonomy, a sense of purpose ( Bernard, 1993 ), motivation and goal orientation, positive use of time, family life, and learning environment ( McMillan and Reed, 1994 ). The potential for building resilience, together with Munro and Pooley's (2009) suggestion that resilience may mediate adversity and success in university students and Hamill's (2003) prioritizing of self-efficacy over other resilience factors, provides the major premise for the present study examining academic self-efficacy (ASE) as a factor influencing student responses to academic adversity.

Resilience and Self-efficacy

Waxman et al. (2003) proposes that academic resilience research needs to examine indicators of resiliency in order to identify what processes can promote protective mechanisms and calls for more affective and motivational training for students to assess their impact on students' affective and motivational outcomes. Aiming to provide a more “expansive” analysis of the factors related to academic resilience, Martin and Marsh (2006) reported self-efficacy, planning, persistence, anxiety, and uncertain control as predictors of academic resilience. Using class participation (behavioral) and enjoyment at school (cognitive-affective) as educational outcome constructs and general self-esteem (global-affective) as a psychological outcome construct, Martin and Marsh hypothesized that the outcome constructs were consequential to students' capacity to effectively deal with challenge, adversity and setbacks experienced in a school setting. As hypothesized, academic resilience was the strongest—relative to the other five motivational and engagement factors—predictor of each of the outcome measures. Analysis to determine students' profiles according to academic resilience revealed that resilient students were high in self-efficacy, persistence and planning and low in anxiety and uncertain control. Hamill (2003) also reported self-efficacy as an important characteristic that distinguished resilient and non-resilient 16–19 year old students.

The pursuit of those factors that distinguish resilient from non-resilient individuals and the promotion of resilience have been at the center of existing research in the field resilience ( Hamill, 2003 ). There is sufficient evidence indicating that self-efficacy is one resilience factor worthy of further study in this respect. Self-efficacy emerged as a central facet in Albert Bandura's Social Cognitive Theory, where is it described as “the belief in one's capabilities to organize and execute the course of action required to manage prospective situations” ( Bandura, 1995 , p. 2). In educational studies, individual differences in perceived self-efficacy have often been shown to be better predictors of performance than either previous achievement or ability ( Cassidy, 2012 ).

Like resilience, self-efficacy is context specific and seems particularly important when individuals face adversity, when positive self-efficacy beliefs are associated with increased motivation and perseverance ( Bandura, 1997 ; Bandura et al., 2001 ) and an increased likelihood of rejecting negative thoughts regarding own capabilities ( Ozer and Bandura, 1990 ).

Self-efficacy is considered to be the foundation of human agency ( Bandura et al., 1999 ) and is referred to as an important protective factor regulating human functioning and emotional wellbeing through cognitive, motivational, affective, and selective processes ( Hamill, 2003 ). And whilst Bandura (1993) does describe how self-efficacy operates to contribute toward academic development—stating that students' beliefs in their efficacy to regulate their own learning and master academic activities determine their aspirations, level of motivation and academic accomplishment—there is a lack evidence-based detail accounting for exactly what high self-efficacious individuals do that impacts positively on academic outcomes; as noted by Hamill (2003) , despite an abundance of self-efficacy focussed research, relatively little work has examined how self-efficacy relates to resilient behaviors exhibited in response to adversity.

Present Study

Operationalizing academic resilience as students' cognitive-affective and behavioral responses to academic adversity, the present study seeks to establish examples of context-specific resilience factors and resilience responses to academic adversity. Self-efficacy has been identified as a key construct in previous studies examining factors affecting academic achievement (e.g., Cassidy, 2012 ), where high self-efficacy is commonly reported as associated with better academic performance. What has not been clearly established in these studies are the specific responses of self-efficacious students to instances of academic adversity, when self-efficacy beliefs are particularly relevant because of their association with increased motivation and perseverance ( Bandura, 1997 ) and resistance to negative thought ( Ozer and Bandura, 1990 ). Hamill (2003) has explored this issue but using generalized measures of self-efficacy and coping responses in the context of general stressful life events in a small sample of 16–19 year old students—limitations which Hamil partly acknowledges. Hamil reported an association between self-efficacy and resilience, adding support to the merits of the present study and its aim of uncovering differences in context-specific resilience responses adopted by self-efficacious and non-self-efficacious students, and the study's longer-term objective of promoting resilient responses in students.

Riley and Masten (2005 , p. 13) define resilience as “referring to patterns of positive adaptation in the face of adversity…,” and describe resilience as requiring “that significant adversity or threat to adaptation or development has occurred” and “that functioning is okay, either because adequate adaptation was sustained over a period of adversity or because recovery to adequate functioning has been observed.” In order to represent the key constituents of resilience (i.e., adversity and positive adaption) in a context-specific and authentic manner to serve the purposes of the study, an academic adversity case vignette and a response to academic adversity scale (Academic Resilience Scale-30) were developed [see Section Academic Resilience Scale-30 (ARS-30)].

The content of the case vignette was intended to represent adversity in a context-specific academic setting that undergraduate students would consider authentic despite its hypothetical nature. The vignette describes academic failure and its wider impact as an example of authentic adversity for students. Although there is some debate in the existing literature on the specific effects of, and perceptions of, negative feedback (e.g., Kluger and DeNisi, 1996 ), reference in the vignette to failure and the wider negative impact of such failure was considered to be sufficient to instill academic adversity. There are two versions of the vignette presented in Section Academic Resilience Scale-30 (ARS-30), personalized and vicarious . The personalized vignette asks that participants imagine that they are personally facing adversity and how they would respond, whilst the vicarious vignette asks participants to imagine that a fellow student is facing adversity and how that student should respond. The vicarious vignette was developed in order to explore any differences between responses to personal adversity and responses advocated for a fellow student facing adversity, and to examine in what way self-efficacy beliefs are associated with any differences. Gaining such insight may be valuable for resilience building interventions, whereby any differences in personal and advocated responses can be used to highlight self-limiting responses or belief systems that may also limit students' capacity for acting in advocate roles, including peer-assisted learning programmes.

Based on previous studies it is anticipated that findings will reveal a positive relationship between ASE and academic resilience, although it is unclear which of the 30 responses to academic adversity will present as most pivotal in defining differences in academic resilience between lower and higher ASE students. Because self-efficacy is a “self” construct most closely related to personal functioning, it is anticipated that any association between self-efficacy and resilience will be more pronounced in responses to the personal adversity vignette as compared to the vicarious adversity vignette.

Participants and Design

The sample comprised 435 British undergraduate students (see Tables 1 , 2 ). The study adopted a self-report questionnaire-based design with correlational and between-subjects components. Academic self-efficacy and academic resilience were measured during a single data collection point in participants' first, second, or third year as undergraduates. Gender, age, and year of study data were also collected. Whilst the gender bias evident within the sample was not desirable, that over 80% of the sample were female is representative of a typical student intake, at least in psychology ( Bourne, 2014 ).

Table 1. Total sample details .

Table 2. Sample details by vignette group .

General Academic Self-efficacy Scale (GASE)

This is 23 item context-specific scale measuring student ASE. The General Academic Self-Efficacy Scale was adapted from the Health Student Self-Efficacy (HSSE) Scale originally developed by Eachus (1993) as a measure of self-efficacy beliefs in students on health-related courses involving clinical training and practice. Cassidy and Eachus (2002) revised the HSSE scale, removing reference to clinical placements, and developed the GASE scale for use with general undergraduate student populations. Participants record their level of agreement with each of the 23 items along a 9-point Likert scale from strongly agree to strongly disagree. The scale contains both positively and negatively worded items, examples of which include “I know I have the ability to complete this course successfully” and “I have some doubts about my ability to grasp some of the topics taught on this course.” Scores for negatively worded items are reversed so that a high GASE score indicates high (or positive) ASE. Scores for the 23 items are summed providing a total scale score between 23 and 207. Cassidy and Eachus (2002) report high internal (α = 0.86) and external ( r = 0.71) reliability for the GASE scale and construct validity is further demonstrated through significant correlations with academic locus of control and computer user self-efficacy. A similarly high alpha (α = 0.84, N = 434) is reported in the present study.

Academic Resilience Scale-30 (ARS-30)

In the absence of a suitable measure of academic resilience, the ARS-30 was developed as a context-specific measure of student response to academic adversity. Scale items represent a sample of relevant positively and negatively phrased behavioral and cognitive-affective responses that participants have to rate as likely or unlikely along a 5-point Likert scale following exposure to the personal or vicarious adversity case vignette:

Personal Vignette : You have received your mark for a recent assignment and it is a “fail.” The marks for two other recent assignments were also poorer than you would want as you are aiming to get as good a degree as you can because you have clear career goals in mind and don't want to disappoint your family. The feedback from the tutor for the assignment is quite critical, including reference to “lack of understanding” and “poor writing and expression,” but it also includes ways that the work could be improved. Similar comments were made by the tutors who marked your other two assignments.

Vicarious Vignette : John has received a mark for a recent assignment and it is a “fail.” The marks John received for two other recent assignments were also poorer than he would want as he is aiming to get as good a degree as he can because he has clear career goals in mind and doesn't want to disappoint his family. The feedback John received from the tutor for the failed assignment is quite critical, including reference to “lack of understanding” and “poor writing and expression,” but it also includes ways that the work could be improved. Similar comments were made by the tutors who marked John's other two assignments.

Scoring of positively phrased items was reversed so that a high ARS-30 score indicated greater academic resilience. Cronbach's alpha for the combined (α = 0.89, N = 432), personalized (α = 0.88, n = 224) and vicarious vignette (α = 0.85, n = 208) all reached acceptable levels indicating internal reliability and construct validity ( Nunnally and Bernstein, 1994 ). Analysis of the relationships between ARS-30 scores and ASE and differences between personal and vicarious responses to adversity further supported the construct validity of the ARS-30 as a measure of academic resilience (see Section Results).

Exploratory factor analysis [principle component with oblique (promax) rotation] was conducted to explore the structure of the ARS-30. Sampling adequacy was verified (KMO = 0.91) and whilst initial analysis revealed seven factors with eigenvalues of 1.0 or above ( Kaiser, 1960 ) explaining 55.75% of the variance, the scree plot inflection ( Cattell, 1966 ) supported retention of only three factors ( Hatcher, 1994 ; Stevens, 2002 ). The three factor model explained 40% of the variance with all items—except one, which loaded at 0.29—loading above 0.3 ( Field, 2014 ). Interpretation of Item-factor clustering suggests that factor 1 represents positive or adaptive responses to adversity, factor 2 represents negative or non-adaptive responses to adversity and factor 3 represents long-term future aspirations. Thus, factors 1 and 2 may simply represent two aspects of the same underlying generalized academic resilience construct. This is partly supported by Schmitt and Stults (1985) and Spector et al. (1997) who report that reverse-phrased items commonly load on different factors, even in the absence of multiple constructs, and by the inter-factor correlation (−0.45) between factors 1 and 2. That factor 3 aligns with closely associated and relevant constructs such as Duckworth's “grit,” which has its basis in long-term goals, suggests that a three factors solution presents an interpretable solution to the ASR-30.

The study was carried out in accordance with the recommendations of the British Psychological Society Code of Ethics and Conduct and the Research, Innovation and Academic Engagement Ethical Approval Panel, University of Salford with written informed consent from all subjects in accordance with the Declaration of Helsinki.

After completing the GASE scale, participants were randomly assigned to one of the adversity vignette conditions and completed the ARS-30 (personal or vicarious). Data collection was anonymous to improve the validity of responses. A median-split approach was used to create discrete groups according to scores on the GASE. Participants with scores equal to or below the GASE sample median of 148 were assigned to the lower ASE group, while participants scoring above the median were assigned to the higher ASE group. Whilst the median-split approach is criticized on the basis of loss of statistical power and the potential for spurious outcomes in cases of multiple variables ( MacCallum et al., 2002 ; Irwin and McClelland, 2003 ), the approach has received support in terms of producing meaningful findings that are understood by, and accessible to, a wider audience where statistical power and effect are not necessarily reduced ( Farrington and Loeber, 2000 ). Thus, the use of dichotomization here is defended on the basis that correlational and regression analysis were also performed for the main analysis using GASE scores as a continuous variable; that the mean difference between groups (30.3) provided, it is suggested, sufficient numerical distance between groups; and the wish to illustrate, in a meaningful way, distinctions between groups in terms of specific responses to adversity.

Significant positive correlations between ASE and academic resilience were observed for the combined vignette groups (medium effect size r = 0.34, Cohen, 1988 ) and for the personal (large effect size r = 0.51) and vicarious vignette groups (small effect size r = 0.21) separately. Academic self-efficacy was a significant predictor of academic resilience explaining 26.2% of variance in resilience in the personal vignette group, 4.6% in the vicarious vignette group, and 14% in the combined groups (see Table 3 ).

Table 3. Zero order correlations and regression analysis with academic self-efficacy (ASE) as a predictor of academic resilience .

A 2(vignette: personal vs. vicarious) × 2(ASE: lower vs. higher) between-subjects factorial ANOVA was conducted to examine differences in academic resilience between personal and vicarious vignette groups as a function of ASE (see Table 4 ).

Table 4. Mean academic resilience scores by vignette group and academic self-efficacy (ASE) Group .

There were significant main effects for vignette group [ F (1, 427) = 101.91, p < 0.001, d = 0.96], such that the vicarious vignette group reported significantly higher academic resilience ( M = 128.51, SD = 11.47) than the personal vignette group ( M = 116.25, SD = 14.07), and for ASE group [ F (1, 427) = 38.26, p < 0.001, d = 0.58], with the higher ASE group reporting significantly higher academic resilience ( M = 126.16, SD = 11.99) than the lower ASE group ( M = 118.20, SD = 15.20). A significant interaction effect [ F (2, 427) = 10.9, p < 0.001, d = 0.33] indicated that the influence of ASE on increasing academic resilience was significantly greater in the personal vignette group, where the effect size was large ( d = 0.86), than in the vicarious vignette group, where the effect size was small ( d = 0.30) (see Figure 1 ).

Figure 1. Mean academic resilience by ASE group vignette type .

Both lower and higher ASE groups scored higher academic resilience when responding to the vicarious vignette than when responding to the personal vignette, though the effect size was larger for the lower ASE group (large ES d = 1.21) than for the higher ASE group (medium ES d = 0.71).

Table 5 shows ASR-30 (personal vignette) mean item scores by ASE group (lower and higher). A One-way MANOVA was performed on these data with ASE group (lower vs. higher) as the independent variable and ASR-30 item scores as the dependent variables. There was a significant multivariate effect [ F (1, 222) = 2.971, p < 0.001] and significant univariate effects. Significant univariate effects are denoted by “*”and reflect scores indicating significantly higher academic resilience for the higher ASE group on all items except items 1, 6, 14, 26, and 29, where any differences were non-significant ( p > 0.05). Effect sizes were medium ( d ≥ 0.5) for 12 of the items and small ( d ≥ 0.2 < 0.05) for the remaining 13 items where a significant group difference was reported.

Table 5. Academic resilience scale (personal vignette) item summary statistics by academic self-efficacy (ASE) group .

Table 6 shows ASR-30 (vicarious vignette) mean item scores by ASE group (lower and higher). A One-way MANOVA was performed on these data with ASE group (lower vs. higher) as the independent variable and ASR-30 item scores as the dependent variables. The multivariate effect was non-significant [ F (1, 205) = 0.659, p >0.05]. Significant univariate effects were only found for items 6, 11, 15 and 24 ( p < 0.05) and reflect scores indicating significantly higher academic resilience for the higher ASE group, although effect sizes were small or minimal ( d < 0.2).

Table 6. Academic resilience scale (vicarious vignette) item summary statistics by academic self-efficacy (ASE) group .

Table 7 shows ASR-30 mean item scores by vignette group. A One-way MANOVA was performed on these data with vignette group (personal vs. vicarious) as the independent variable and ARS-30 item scores as the dependent variables. There was a significant multivariate effect [ F (1, 430) = 14.929, p < 0.001] and significant univariate effects. Significant univariate effects are denoted by “*” and “**” and reflect scores indicating significantly higher academic resilience for the vicarious group on all items except items 5 and 19 where academic resilience was significantly lower in the vicarious group (with minimal or small effect size) and items 1, 2, 10, 11, 13, 15, and 17, where any differences were non-significant ( p >0.05). Effect sizes were large ( d ≥ 0.8) for one item, medium ( d ≥ 0.5) for seven items, small ( d ≥ 0.2) for 12 items, and minimal ( d < 0.2) for the remaining three items where a significant group difference was reported.

Table 7. Academic resilience scaleitem mean scores by vignette group .

Figure 2 shows that while the difference in mean academic resilience scores between the personal and vicarious vignette groups was significant [ t (430) = 9.908, p < 0.001], with a large effect size ( d = 0.96), there was no significant difference in ASE scores [ t (432) = 0.356, p > 0.05].

Figure 2. Mean academic resilience by vignette group .

Age, Gender, and Year of Study Analysis

Gender and year of study analysis did not reveal any significant differences in academic resilience ( p > 0.05). Correlational analysis did not reveal any significant association between age and academic resilience ( p > 0.05), although a small significant correlation between age and ASE [ r (429) = 1.58, p < 0.001] was reported.

Overall results support the hypothesis that ASE is associated with, and a predictor of, academic resilience. Significant positive correlations between ASE and academic resilience were reported for both the personal and vicarious vignettes, although effect size was large for the personal vignette group and small for the vicarious vignette group. Analysis of ASE as a predictor of academic resilience also led to significant results for each of the vignette groups, with the greatest variance in academic resilience (26.2%) accounted for in the personal vignette group compared to only 4.6% in the vicarious group. Although previous studies have reported self-efficacy as an important contributory factor for resilience ( Hamill, 2003 ; Martin and Marsh, 2006 ), the present study offers additional insight into the context-specific interplay of these constructs. As advocated by Pajares (1996) and by Riley and Masten (2005) , Liddle (1994) and Waxman et al. (2003) , both self-efficacy and resilience were measured as context-specific constructs and in relation to—it is argued here—an authentic adverse situation and relevant adaptive responses. In both general and context-specific terms, findings support the relevance of self-efficacy beliefs to individual psychological resilience; having positive self-efficacy beliefs is likely to contribute toward increased resilience in students.

Once a relationship between ASE and academic resilience was established, further analysis sought to identify differences between lower and higher self-efficacy students in their specific responses to adversity. As anticipated, higher self-efficacy students reported significantly higher academic resilience for both case vignettes, although a significant interaction effect indicated greater influence of self-efficacy for the personal vignette, where the effect size was large, than for the vicarious vignette, where the effect size was small. The greater influence of self-efficacy on personal resilience is unsurprising in light of Bandura's (1993) account of self-efficacy as a mechanism of personal agency that makes causal contributions to own functioning. Analysis of responses to individual items on the Academic Resilience Scale-30 (personal vignette) showed that the higher self-efficacy group scored significantly higher on 25 of the 30 items, with small to medium effect sizes reported (see Table 5 ). This level of analysis highlights specific examples of responses to adversity where self-efficacious students responded in a more adaptive manner, providing a basis to better understand the precise nature of the influence of self-efficacy on resilience and offering a potential basis for interventions promoting resilience. Conversely, the items where there was no significant difference between self-efficacy groups are of little value in differentiating resilient and non-resilient students, at least on the basis of ASE beliefs. Responses to these items could still be adaptive or non-adaptive, conferring resilience or lack of it, but may be determined by individual difference constructs or processes other than self-efficacy. Similar analysis of responses to the vicarious adversity vignette revealed significant differences in only 4 of the 30 items, all with small effect sizes. This further supports the nature of self-efficacy as a mechanism for personal (human) agency and illustrates the limited influence of self-efficacy beliefs on the potential to perform academic advocacy roles, such as peer assisted learning mentors.

Results comparing responses to personal and vicarious vignettes revealed a significant difference and large effect size, with students reporting significantly higher resilience for the vicarious adversity vignette (see Figure 2 ). This effect was not explained by group differences in self-efficacy. That students advocate more positive adaptive responses to adversity experienced by a peer provides potentially valuable insights for resilience building. In general terms, it supports the value of peer mentoring and peer assisted learning and lessens concerns that negative belief systems might impact negatively on academic advocacy. In fact results suggest that students, including those with lower self-efficacy, are likely to be a positive source of encouragement and resilience for peers who are experiencing challenge and adversity. This is an important finding given continued growth in the implementation, evaluation and reputed benefits of peer assisted learning initiatives ( Ginsburg-Block et al., 2006 ; Smith et al., 2007 ; Romito, 2014 ). In more specific terms, results suggest that students are aware of what are and are not adaptive responses and have the potential to exhibit greater personal resilience than they may be currently exhibiting. One aspect of interventions promoting resilience could involve highlighting this difference between personal and vicarious resilience and encouraging students to reflect on their own reasons for advocating greater resilience for their peers and to explore the potential to move toward greater personal adoption of the responses advocated for their peers. Using examples of differences in specific responses, where significant differences in 23 of the 30 items are reported (see Table 7 ), could be helpful in this respect, enabling students to focus on areas where responses could be more adaptive.

Whilst academic resilience was significantly higher for the vicarious vignette for both lower and higher self-efficacy groups, the difference between personal and vicarious vignettes was greatest for lower self-efficacy students (see Figure 1 ). One interpretation of this is that lower ASE students have more to gain than students with higher self-efficacy from reflecting on how they respond to adversity experienced by a peer and using this to help promote more adaptive responses to personal adversity.

Consistent with previous studies ( Munro and Pooley, 2009 ; McLafferty et al., 2012 ), no significant differences in academic resilience according to age, gender, or year of study were observed in the present study. That females were heavily underrepresented in the sample does limit confidence in this particular finding, particularly in light of studies that do report greater academic resilience in female undergraduates (e.g., Allan et al., 2014 ).

Limitations

Although the study offers advances in applied academic resilience research and practice, some important limitations need to be considered when interpreting the results and conclusions of the study. Resilience studies commonly operationalize adversity in terms of difficult or unpleasant situations or experiences. It is suggested that the case vignettes developed for the study represent adversity in a relevant and authentic way for the purposes of studying academic adversity. Others—Martin and Marsh (2008 , 2009) and Martin (2013) for example—may argue that the vignette is not sufficiently traumatic, stressful or prolonged to adequately represent adversity as it is routinely represented in resilience studies. The ARS-30 is a newly developed measure of academic resilience and although findings do support its reliability and validity, further development work, particularly examining its predictive validity, will add to its integrity as a measure of academic resilience. Comparisons of personal and vicarious resilience were made between subject groups, introducing individual difference error; within-subject comparisons would provide a more robust basis upon which to draw conclusions regarding this aspect of the study. Also, given the differences that emerged between responses to the personal and vicarious case vignettes, those parts of the analysis that combine resilience response data across the vignettes should be treated with caution, focussing instead on analyses presented for the vignettes independently.

Future Directions

Whilst the lack of consensus that exists in terms of conceptualizing and operationalizing resilience ( Maclean, 2004 ; Friedland, 2005 ) is less pronounced within the narrower field of academic resilience (see Dweck, 2010 ; Duckworth, 2013 ; Martin, 2013 ), it is nonetheless suggested that there are two key areas of development necessary for increased impact of future general and academic resilience research. The first should address how best to capture aspects of resilience in a valid and reliable construct measure or measures. Grotberg (1997) for example summarizes the three aspects of resilience as: “I have” (e.g., trusting and loving relationships, encouragement to be independent); “I am” (e.g., proud of myself, responsible, hopeful); and “I can” (e.g., manage my feelings, solve problems). Similarly, caring relationships, good problem solving and intellectual functioning are identified by Masten and Coatsworth (1998) as factors promoting competency in individuals faced with adversity. The second area of development should continue to address the issue of identifying key factors and constructs associated with resilience. Discussing building resilience in vulnerable and disadvantage children and young people, Maclean (2004) identifies several familiar “qualities” or factors associated with resilience. These include initiative and insight, optimism, intellectual ability, placid temperament, trust, autonomy and decision making, humor, identity, social support, education, attainment, self-esteem and self-efficacy. Maclean goes on to raise the issue of the lack clarity surrounding how practioners can help individuals become more resilient; identifying associated constructs, as Duckworth's (2013) and Dweck's (2006 , 2010 ) have done with their constructs of grit and mindset, will assist the development and implementation of interventions promoting resilience, both in general and academic contexts. Evaluating new interventions is clearly a further avenue for research exploring academic resilience. Other avenues include longitudinal cohort studies examining the predictive value of academic resilience against outcomes including achievement, student satisfaction, retention and wellbeing.

In light of a recent impetus for intrapersonal research in education (Network on Intrapersonal Research in Education, 2015 ), future studies should consider examining both inter-individual and intra-individual variation in academic resilience. Such studies would reveal the extent to which population data can be generalized to patterns of resilience observed in individual students (and vice-versa), and would be particularly valuable in helping explore process aspects of resilience, as opposed to outcomes measures such as grade point average, in the evaluation of interventions or where adverse situations occur and are time-bound. Windle et al.'s (2011) description of resilience as the process of negotiating, managing and adapting to significant sources of stress or trauma emphasizes the importance of adopting such a process-focused view of resilience.

Conclusions

The present study sought to identify factors that contribute, in a meaningful way, to academic resilience and to examine how such factors influence specific, and meaningful, responses to academic adversity. Consistent with previous studies ( Hamill, 2003 ; Martin and Marsh, 2006 ), findings presented support ASE as predictive of academic resilience and go beyond earlier studies in identifying specific examples of responses to academic adversity, where lower and higher self-efficacy students respond in a differentially adaptive manner. As such, it is suggested that self-efficacy training, already shown to be effective in an educational context ( Siegle and McCoach, 2007 ), offers one approach to building academic resilience in students. Illustrating how self-efficacy influences specific responses to adversity, and the propensity to advocate greater resilience for peers facing adversity, should form another—metacognitive—aspect of resilience building for students. As Martin and Marsh (2006) have stated, identifying the specific facets comprising academic resilience will support an enhanced and more targeted approach to interventions aimed at enabling students to cope with the demands of academic life.

Conflict of Interest Statement

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

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Keywords: resilience, self-efficacy, adversity, student, learning

Citation: Cassidy S (2015) Resilience Building in Students: The Role of Academic Self-Efficacy. Front. Psychol . 6:1781. doi: 10.3389/fpsyg.2015.01781

Received: 29 May 2015; Accepted: 05 November 2015; Published: 27 November 2015.

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Copyright © 2015 Cassidy. 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) or licensor 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: Simon Cassidy, [email protected]

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Improving students' mathematics self-efficacy: A systematic review of intervention studies

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Self-efficacy is an integral part of personal factors that contributes substantially to students' success in mathematics. This review draws on previous intervention studies to identify, describe, and expose underlying mechanisms of interventions that foster mathematics self-efficacy. The findings show that effective mathematics self-efficacy interventions can be categorized into three categories using their underlying mechanisms: those that directly manipulate sources of self-efficacy to foster the construct, and those that either embed self-efficacy features in teaching methods or in learning strategies. Specific examples of interventions that fall in each of these three categories are described including their features and the underlying mechanisms that improve students' mathematics self-efficacy. I argue for the two “most effective” interventions that foster mathematics self-efficacy and their relevance to either pre-university or university students with implications for teaching and learning of mathematics.

Introduction

Research on affect in mathematics education is attracting increased attention such that researchers are proposing a unifying theoretical framework for its various constructs. For instance, Hannula ( 2012 ) proposed a unified theoretical approach to these constructs within mathematics education research community. Some of the constructs that characterize affect in mathematics education research are attitudes toward mathematics, beliefs in mathematics, mathematics anxiety, mathematics emotions, mathematics hate, mathematics joy, and mathematics self-efficacy. Students' mathematics self-efficacy is a crucial construct that has historic significance to affect in mathematics education research (Hannula, 2012 ). Its origin is traceable to Bandura's social cognitive theory that sees human functioning as an emergence of a dynamic interaction system between personal (e.g., self-efficacy), behavioral (e.g., use of effective approaches to learning), and environmental (e.g., teacher's feedback) determinants (Bandura, 1997 , 2012 ). That is, students with high self-efficacy are quick to engage in behavioral activities (e.g., using efficient approaches to learning) that enhance their successful execution of the presented tasks. In turn, quality feedback from teachers or peers is a crucial factor that reinforces self-efficacy (Bandura, 1997 ). This dynamic interaction between the personal, behavioral, and environmental determinants characterizes human functioning within and outside formal classroom settings (Bandura, 2001 ).

Self-efficacy is a personal determinant of human functioning is defined as “beliefs in one's capabilities to organize and execute the courses of action required to produce given attainments” (Bandura, 1997 , p. 3). Within the context of mathematics learning, mathematics self-efficacy is conceptualized as “a situational specific assessment of an individual's confidence in her or his ability to successfully perform or accomplish a particular [mathematics] task or problem” (Hackett and Betz, 1989 , p. 262). Mathematics self-efficacy encompasses students' interpretation of their prior attainments, a self-appraisal of their ability, and a personal estimation of subsequent performance on presented mathematics tasks. It is an important construct that determines students' engagement with mathematics tasks. Some students engage in the tasks they feel confident to solve and avoid tasks they believe are out of their competence level. Mathematics self-efficacy influences students' choices of tasks on which they will expend much effort, it determines students' level of perseverance and the amount of forbearance in difficult situations (Pajares, 1996 ; Zakariya et al., 2019 ). As such, mathematics self-efficacy is a self-evaluation of students' competence about the presented mathematics tasks which constitutes an internal drive for the successful completion of the task.

Given the importance of mathematics self-efficacy to students' learning experience, several interventions on the construct are described in literature (e.g., Siegle and McCoach, 2007 ; Schukajlow et al., 2019 ), mostly by educational psychologists, as a proxy to improve students' learning outcomes in mathematics. However, few studies provide coherent arguments about which and to what extent interventions enhance students' mathematics self-efficacy. This review attempts to fill this gap by drawing on previous intervention studies to identify, describe, and expose underlying mechanisms of interventions that foster mathematics self-efficacy.

Task specificity of mathematics self-efficacy

There is an accumulation of both empirical and theoretical evidence that suggests that mathematics self-efficacy is best operationalised and measured using task specific instruments. Researchers argued that the task specificity of mathematics self-efficacy must be duly accounted for to enhance predictive power of the construct (e.g., Bandura and Schunk, 1981 ; Hackett and Betz, 1989 ; Pajares and Miller, 1995 ; Klassen and Usher, 2010 ; Toland and Usher, 2015 ). The implication of the task specificity goes beyond the predictive power of mathematics self-efficacy to the development of its measures. Students are more likely to accurately report their convictions to be able to solve mathematics tasks when sample tasks are presented to them than the situation in which sample tasks are not presented. Borgonovi and Pokropek ( 2019 ) investigate this fact and found a non-trivial link between task exposure and mathematics self-efficacy among secondary school students. This task-specific measure of mathematics self-efficacy makes it different in conceptualization as well as in measurement from a related construct called mathematics self-concept. The formal is task-specific while the latter is the belief of self-worth associated with one's perceived competence without any reference to neither specific situation nor to specific task (Pajares and Miller, 1994 ). As such, several measures of mathematics self-efficacy are tailored toward specific tasks in mathematics rather than general mathematics (Hackett and Betz, 1989 ; Pajares and Miller, 1995 ; Kranzler and Pajares, 1997 ; Zakariya et al., 2019 ). For instance, the calculus self-efficacy inventory developed by Zakariya ( 2019 ) requires respondents to rate their confidence to solve some presented calculus exam-like tasks on a scale of 0 to 100.

Sources of mathematics self-efficacy

Apart from the conceptualization, task specificity, and measures of mathematics self-efficacy, a sizeable number of studies are reported on the sources of mathematics self-efficacy. Drawing on Bandura's social cognitive theory, four sources of mathematics self-efficacy are theorized, investigated, and measured in literature (e.g., Lent et al., 1991 ; Usher and Pajares, 2009 ; Gao, 2019 ). Students build their self-efficacy based on interpretations of events that emanate from four sources: “mastery experience,” “verbal/social persuasions,” “physiological or affective states,” and “vicarious experience” (Bandura, 2012 ). The mastery experience encapsulates students' interpretations of their previous academic attainments in mathematics. It is the strongest source of mathematics self-efficacy (Zientek et al., 2019 ). Success reinforces self-efficacy while failure mars it. Students that accomplish a mathematics task, especially a difficult task for others, interpret their success in a positive way such that the interpretation elevates their judgement of competence in mathematics. In contrast, students' interpretation of failures on mathematics tasks tend to lower the judgement of their competence in mathematics (Usher and Pajares, 2009 ). It is crucial to remark that students' interpretations of the same academic achievement (e.g., same grades) may differ, and so does the impact of such achievement on individual's mathematics self-efficacy. Thus, individual interpretation of mastery experience is pertinent to mathematics self-efficacy rather than the objective grade in itself (Lopez et al., 1997 ).

Social persuasion is a source of mathematics self-efficacy that students make while listening to verbal persuasion from other people. The timely encouragement from teachers, parents, peers, and more proficient adults are likely to foster students' confidence when dealing with challenging situations. On the other hand, negative remarks from others undermine students' mathematics self-efficacy in the face of obstacles. In fact, the influence of social persuasion on mathematics self-efficacy is more pronounced in weakening self-efficacy rather than bolstering it (Usher and Pajares, 2009 ). Empirical evidence also suggests that social persuasion is a substantive source of mathematics self-efficacy as after mastery experience (Lopez et al., 1997 ; Yurt, 2014 ). However, some researchers (e.g., Lau et al., 2018 ) show that social persuasion predicts self-efficacy better than mastery experience.

The physiological or affective source of mathematics self-efficacy entails the self-evaluation of competence on mathematics tasks that draws on varying levels of students' emotions such as anxiety, mood, attitudes, and physiological arousals such as burnout, fatigue, and stress. Students who feel secured, relaxed, and emotionally stable while engaging in mathematics activity tend to judge their competence on the mathematics activity very highly. In contrast, emotional instability, burnout, fatigue, and stress play crucial role in weakening students' evaluation of their competence on mathematics tasks. As stated by Usher and Pajares ( 2009 ) “increasing students' physical and emotional wellbeing and reducing negative emotional states strengthens self-efficacy” (p. 90).

The vicarious experience is a source of self-efficacy that relates to students' interpretation of others' experience (Matsui et al., 1990 ). Observing comparable others who succeed in completing a mathematics task is a crucial source of mathematics self-efficacy (Usher and Pajares, 2009 ). Students can draw on the successes or failures of their peers, colleagues, and comparable others in a mathematics task to make self-evaluation of their competence on the task (Usher and Pajares, 2009 ). Among the four sources of mathematics self-efficacy, evidence shows that the vicarious experience exhibits the least influence on self-efficacy (Lopez et al., 1997 ; Loo and Choy, 2013 ). However, some researchers (e.g., Usher and Pajares, 2008 ) argued that the inclusion of either peers or adults and not both in measures of vicarious experience can be ascribed to its least rank among the sources of mathematics self-efficacy. Interestingly, all the four sources of mathematics self-efficacy predict, though at varying strengths, not only the self-efficacy but also students' achievements in mathematics (Usher and Pajares, 2008 ; Zientek et al., 2019 ).

Self-efficacy and other affective factors

Self-efficacy predicts and it is predicted by factors such as mathematics self-concept, mathematics anxiety, interest, emotional support, motivational processes, and students' approaches to learning (Pajares and Miller, 1994 ; Lopez et al., 1997 ; Akin and Kurbanoglu, 2011 ; Zakariya et al., 2020 ). There is a substantial correlation between self-efficacy and mathematics self-concept, interest, and perceived usefulness of mathematics (Pajares and Miller, 1994 ; Lopez et al., 1997 ). Also, previous studies (e.g., Akin and Kurbanoglu, 2011 ; Rozgonjuk et al., 2020 ) show that there is a bidirectional relationship between mathematics anxiety and mathematics self-efficacy. That is, students with high self-efficacy tends to exhibit low mathematics anxiety. In turn, students with high mathematics anxiety are associated with low self-efficacy on mathematics tasks. More so, Skaalvik et al. ( 2015 ) show that students' perception of emotional support received from teachers predicts mathematics self-efficacy which in turn predicts motivational processes such as effort expends on mathematics tasks, persistence on difficult mathematics problems, intrinsic motivation, and help seeking disposition. On the other hand, Özcan and Eren Gümüş ( 2019 ) show that mathematics motivation predicts mathematics self-efficacy which in turn predicts retrospective metacognitive experience i.e., students' narrative of their metacognitive activities after solving a mathematics task.

Evidence shows that mathematics self-efficacy is substantially related to students' approaches to learning mathematics (e.g., Diseth, 2011 ; Ardura and Galán, 2019 ; Zakariya et al., 2020 ). In fact, Zakariya et al. ( 2020 ) show that there is a potential causal relationship between mathematics self-efficacy and approaches to learning first-year calculus course. That is, students with high mathematics self-efficacy adopt deep approaches to learning first-year calculus while those with low mathematics self-efficacy adopt surface approaches to learning the course (Zakariya et al., 2020 ). Thus, mathematics self-efficacy influences students' processes and strategies with which they study for mathematics. An in-depth understanding of the underlying mechanism of factors that affect and are affected by mathematics self-efficacy are crucial for developing interventions. As such, the relationship between mathematics self-efficacy and some personal factors has direct implications for this review.

Self-efficacy and performance in mathematics

Several researchers have thoroughly investigated the association between mathematics self-efficacy and students' performance in mathematics. The performance in mathematics, here, means students' examination scores or grades in mathematics courses that they followed. Their findings show that mathematics self-efficacy predicts performance better than mathematics anxiety, mathematics self-concept, mental ability, prior mathematics knowledge, and perceived utility of mathematics (Pajares and Miller, 1994 ; Pajares and Kranzler, 1995 ; Özcan and Eren Gümüş, 2019 ). Also, mathematics self-efficacy predicts performance in mathematics better than intelligence test scores, personality traits (i.e., agreeableness, conscientiousness, emotional instability, extraversion, and openness), and self-esteem (Zuffianò et al., 2013 ). There seems to be a consensus that mathematics self-efficacy has a substantial positive association with students' performance in mathematics (Pajares and Miller, 1995 ; Yurt, 2014 ; Roick and Ringeisen, 2018 ; Zakariya, 2021 ). That is, high mathematics self-efficacy is associated with high performance in mathematics while low mathematics self-efficacy is associated with poor performance in mathematics. Going a step further, Zakariya ( 2021 ) uses an innovative instrumental variable approach of structural equation modeling to show that students' mathematics self-efficacy on mathematics tasks has a causal relation with students' performance in mathematics.

It is important to mention that the crux of the matter of improving mathematics self-efficacy is to serve as a proxy to improve students' performance in mathematics. As the literature suggests, mathematics self-efficacy does not only predict students' performance in mathematics but also has a potential causal relationship with performance (Pajares and Miller, 1995 ; Yurt, 2014 ; Roick and Ringeisen, 2018 ; Zakariya, 2021 ). A pedagogical implication of this relationship is the opportunity made available to mathematics teachers to improve students' performance in mathematics through reinforcement of mathematics self-efficacy. On the part of the students, a high sense of mathematics self-efficacy mitigates their mathematics anxiety and thereby reduces their risk of failure in mathematics (Rozgonjuk et al., 2020 ; Zakariya, 2021 ). On the one hand, these connections between mathematics self-efficacy and the final learning outcomes (e.g., students' performance in mathematics and risks of failure in mathematics) lay more credence to the utility of interventions that reinforce self-efficacy among students following a mathematics course. On the other hand, these connections also buttress the importance of a systematic review of such interventions.

The research aims

This article reports a systematic review of intervention studies that is aimed at improving students' mathematics self-efficacy. In the preceding sections, I presented the theoretical structures of mathematics self-efficacy, its sources, its crucial contributions to other personal factors and students' performance in mathematics. The discussion in each of these sections points to the significance of altering mathematics self-efficacy such that students' performance in mathematics can improve. Thus, studies are that aimed at altering students' mathematics self-efficacy are crucial not only to students' well-being but also to improved performance in mathematics. Therefore, the purpose of this review is to provide an integrative view of previous intervention studies on mathematics self-efficacy. Therein, I am addressing the following questions:

• What are the interventions that enhance self-efficacy and their underlying mechanisms?
• Which of these interventions has the largest effect on self-efficacy?

The researcher believes that attempts to address these research questions will expose the state of the art on interventions that reinforce mathematics self-efficacy. Since knowledge progression is usually built on existing knowledge, it becomes prudent to critically examine the existing knowledge. A review, analysis, and synthesis of relevant literature will reveal the state of the art as it concerns interventions on mathematics self-efficacy. Surprisingly, there is little research with this intention. Admittedly, there is an earlier review of literature on self-efficacy by Bartimote-Aufflick et al. ( 2015 ). However, they restrict the focus of their review to higher education students, and they consider self-efficacy without a particular reference to mathematics learning. Given that mathematics self-efficacy is task specific as pointed out in the background section, the present review will provide more relevant details to mathematics learning than the one by Bartimote-Aufflick et al. ( 2015 ). More so, researchers, mathematics teachers, mathematics course coordinators, and other stakeholders will benefit from the findings of this review. Such benefits would be in the form of what to do, how to do it, and to what extent do interventions alter mathematics self-efficacy for improved performance in mathematics.

Review process

This review followed a framework proposed by Kitchenham and Stuart ( 2007 ) and developed further by Xiao and Watson ( 2017 ). In this framework, three main stages were identified: planning, conducting, and reporting the review. Figure 1 shows the specifics of each of these stages as they relate to the present review.

Specifics of stages of the review process.

Planning stage

The planning stage of this review started by defining the purpose and scope of the review. The purpose was to provide an integrative view of previous intervention studies on mathematics self-efficacy. This purpose led to the formulation of two research questions. As such, I defined “self-efficacy,” “mathematics,” “statistics,” “mathematics self-efficacy,” and “intervention” as keywords for conducting the literature search. I set the criteria for inclusion of studies to be only intervention studies that focus on self-efficacy of students learning either mathematics or statistics. I restricted the scope of this review to mathematics/statistics learning because of the task-specificity of the construct. Further, I included in this review all types of experimental studies e.g., one group pre-test/post-test experiments, quasi-experiments, and randomized control trials that are available in English language.

Reviewing stage

In the reviewing stage, I searched two main literature databases i.e., Web of Science and ERIC for articles published between 1995–2021, using a combination of keywords that were defined in the planning stage. These two databases were used because of their popularity in education research and the quality of articles they indexed. The search on Web of Science returned 220 publications while the search on ERIC returned 195 publications with a substantial overlap at the time of the literature search. I reviewed the titles and read the abstracts of these publications together with some colleagues using the criteria for inclusion as presented in the previous section. Most of the publications were based on measures of self-efficacy, relationships between self-efficacy with other personal factors, and performance. These publications were excluded from the sample of this review because they were not intervention studies. Also, few of the publications were theses, dissertations, and previously presented conference papers of current journal articles. These publications were excluded from the sample as well because I preferred peer-reviewed articles to the evolving publications in theses and conference papers. This screening process led to 17 peer-reviewed journal articles. Then, we checked the references of each of these 17 articles for relevant studies. An additional four articles were identified through this snowballing approach to make 21 peer-reviewed journal articles. We read each of the 21 articles extensively to assess the quality and extract relevant data for this review. The extracted data were then categorized, synthesized, and analyzed to make a coherent argument for this review.

Reporting stage

This is the last stage of the review process. I assimilated the results such that the two research questions were addressed. Further, I discussed some implications of the findings for researchers, mathematics teachers, mathematics course coordinators, and other stakeholders that are involved in the teaching and learning of mathematics.

Results and discussion

Interventions that enhance mathematics self-efficacy.

To identify interventions that enhance mathematics self-efficacy, and as such to address the first research question, I provide a summary of findings from the 21 reviewed articles. Table 1 gives a summary of these results. One can observe from Table 1 that 16 of the 21 reviewed studies attributed significant increase in mathematics self-efficacy to the interventions reported. However, if we exclude single-group experiments and case-study experiments due to their high susceptibility to internal and external validity threats (Bryman, 2016 ) then 11 studies remain which interventions are worthy of further discussion. These studies are marked with asterisks ( * ) in Table 1 and in the reference list. Also, further reading suggests that the study by Huang et al. ( 2020 ) is an improvement on their two earlier studies (Huang, 2017 ; Huang and Mayer, 2018 ). As such, only the latest study is discussed in this review. There are then nine studies, four of which are quasi-experimental research (Ramdass and Zimmerman, 2008 ; Bonne and Johnston, 2016 ; Kandil and Işiksal-Bostan, 2019 ; Kohen et al., 2019 ) and the remaining five studies are randomized controlled experiments (Luzzo et al., 1999 ; Siegle and McCoach, 2007 ; Cordero et al., 2010 ; Brisson et al., 2017 ; Huang et al., 2020 ).

Summary of findings of the reviewed articles.

RCE-randomized control experiment, QE-quasi experimental, FCE-focused group experiment, d-Cohen's effect size, η 2 -partial eta squared, β-standardized regression coefficient, and r-nonparametric correlation coefficient. The four studies that reported sustained effects of their interventions are marked with the suffix

Content analysis of the nine intervention studies shows that the interventions can be grouped and discussed according to their underlying mechanisms. Based on the findings of this analysis, three categories emerge: Interventions based on mathematics self-efficacy sources (IbMSES), instructional-based interventions (IbI), and learning-based interventions (LbI). The criteria for inclusion of a study in the IbMSES category are that the study manipulates at least a source of mathematics self-efficacy, and the source(s) is (are) explicitly stated. As for the IbI and LbI categories, the criteria are that the studies embed mathematics self-efficacy sources/strategies in the teaching and learning of mathematics, respectively. Admittedly, it is difficult to separate learning from teaching. As such, the IbI and LbI categories appear similar but at the same time different.

Interventions based on self-efficacy sources

These interventions are based on manipulating one or more sources of mathematics self-efficacy such that the students' mathematics self-efficacy can be enhanced. The first intervention is this category is the mathematics relevance intervention by Brisson et al. ( 2017 ) with an effect size of 0.16 that was sustained over a period of 6 weeks. The most effective treatment group in their experiment, in terms of fostering mathematics self-efficacy, is the quotation condition. Students in the quotation condition engaged in teacher-led presentations that focus on confidence reinforcement and relevance of mathematics to real-life situations. Thereafter, students engage in an in-class self-reflection on relevance of mathematics to daily lives by reading interview quotations from young adults. This intervention is followed-up by two homework short intervention reinforcements that focus on recalling aspect of in-class self-reflection and self-evaluation of arguments about utility of mathematics. The basic mechanism of fostering mathematics self-efficacy in quotation treatment condition lies in using the utility of mathematics to provide vicarious experience to students.

Luzzo et al. ( 1999 ) combined performance accomplishment and vicarious experience to design interventions that foster mathematics self-efficacy with an effect size of 0.51 that was sustained over a period of 4 weeks. In a similar manner, Cordero et al. ( 2010 ) combined performance accomplishment with belief perseverance to design an intervention that fosters mathematics self-efficacy by with an effect size of 0.09 that was sustained over a period of 6 weeks. The idea behind performance accomplishment intervention in both studies requires students to solve some mathematics problems, mark their answers by themselves, and then rate their accomplishment in attaining a pre-set criterion for success before the experiment. The vicarious experience intervention by Luzzo et al. ( 1999 ) requires students to watch a video presentation of a senior colleague(s) that has previously followed the target mathematics course. In the videotaped presentation, the models share their experience while following the course and how the course has helped them in their career aspirations. On the other hand, the belief perseverance intervention by Cordero et al. ( 2010 ) requires students to write a proposal that justifies their suitability for a fully-funded scholarship that centers around their belief of success in demanding mathematics activities. The basic mechanisms that foster mathematics self-efficacy in these interventions are self-persuasion and vicarious experience.

Instructional-based interventions

These are interventions that foster mathematics self-efficacy through manipulations of teaching methods. Inquiry-based instruction enriched with Origami was proved effective in enhancing students' self-efficacy on mathematics tasks (Kandil and Işiksal-Bostan, 2019 ). Origami has to do with folding papers for instructional purpose with relevance to geometry (Kandil and Işiksal-Bostan, 2019 ). Kohen et al. ( 2019 ) show that incorporating dynamic visualization activity e.g., use of GeoGebra application, into an active instructional method is another effective way to enhance students' mathematics self-efficacy. They taught some students analysis of functions using the dynamic visualization instruction and found that mathematics self-efficacy is improved afterwards. The underlying mechanisms that foster mathematics self-efficacy in these interventions are the additional reinforcement offered by Origami and the dynamic digital software, respectively.

From teachers' perspective to students' enhancement of mathematics self-efficacy, some researchers reported interventions that focus on manipulating teacher professional development programmes or training (Siegle and McCoach, 2007 ; Bonne and Johnston, 2016 ). The intervention study by Siegle and McCoach ( 2007 ) show that teacher training that centers around goal setting, quality teacher feedback, and peer modeling can foster mathematics self-efficacy. The goal setting involves activities that remind students of their mastery experience. The teacher feedback serves as social persuasion through complimenting students' effort, and the peer modeling provides vicarious experience to the students. In a similar manner, Bonne and Johnston ( 2016 ) show how pedagogical strategies can be used to enhance mathematics self-efficacy. Some of these strategies are sharing instructional objectives with students, reminding students of their mastery experience, encouraging students to attribute failure to lack of sufficient effort, guiding students through coping mechanisms in difficult situations, encouraging social persuasion, and using similar ability (learning needs) peers as models. These sources of mathematics self-efficacy are embedded in the teacher training to provide an effective intervention for enhancing the construct.

Learning-based interventions

These are interventions that foster mathematics self-efficacy through manipulations of students' learning strategies. An effective intervention in this category is the integration of four self-efficacy features – anxiety coping strategy (affective states), modeling example (vicarious experience), mental practice (mastery experience), and effort feedback (social persuasion) – into a computerized example-based learning activity. For instance, Ramdass and Zimmerman ( 2008 ) report an intervention study in which a step-by-step solution strategy was supplemented with self-correcting strategy for improved mathematics self-efficacy. Students in the experimental group are trained on using some strategies to check whether their answers are correct in addition to the step-by-step solution method. The intervention proves effective, and its underlying mechanism lies in using mastery experience coupled with self-persuasion to foster mathematics self-efficacy.

Akin to the manipulation of learning strategy as a proxy to foster students' mathematics self-efficacy is computerized example-based intervention by Huang et al. ( 2020 ). They created a computerized learning environment that students used to learn and practice some statistical skills after following some worked examples. Students in the integrated example-based treatment condition started the experiment by listening to some anxiety coping strategies disguised as instructions. Then, they followed some worked examples that are presented by an animated expert to provide vicarious experience. At the end of each model example, students engaged in some mental practices to provide mastery experience. Students then proceed to solve their presented questions at the end of which are some feedback statements that provide social persuasion to the students. A sample feedback statement is “Your answer is not 100% correct. Don't give up. Focus on the next example-problem pair. Study the example carefully. With hard work, your performance will improve” (Huang et al., 2020 , p. 1018). The basic mechanisms that foster mathematics self-efficacy in this intervention are the four sources of self-efficacy embedded in learning strategy.

Interventions with the highest effect on self-efficacy

To address the research question two of this review, I take a closer look at Table 1 and argue for the intervention with the highest effect on self-efficacy among the presented nine intervention studies. At this stage, three out of the nine studies are screened out from the discussion that follows because their authors do not explicitly report the effect sizes of the interventions (Ramdass and Zimmerman, 2008 ; Kandil and Işiksal-Bostan, 2019 ; Kohen et al., 2019 ). Half of the remaining six studies focus on pre-university students: primary school students with age ranging from 7 to 9 years (Siegle and McCoach, 2007 ; Bonne and Johnston, 2016 ), and lower secondary school students with age ranging from 13 to 14 years (Brisson et al., 2017 ). The other half of the studies focus on university students (Luzzo et al., 1999 ; Cordero et al., 2010 ; Huang et al., 2020 ). As such, if the focus is to improve pre-university students' mathematics self-efficacy then the intervention (self-efficacy features embedded in a teacher training) by Siegle and McCoach ( 2007 ) is expected to have the highest impact on mathematics self-efficacy. The reported effect size is 0.46 for an intervention that lasted for 4 weeks. Interestingly, a step-by-step implementation of this intervention including resources such as videos and teachers' training notes are freely available online ( https://nrcgt.uconn.edu/underachievement_study/self-efficacy/SE_Section0/ ). In the implementation of this recommendation, one should consider the age of participants, the country in which the research was conducted and the associated cultural factors. Moreover, Siegle and McCoach ( 2007 ) did not report the sustained effect of their intervention which is a crucial factor that should be considered in the implementation of this recommendation. For instance, the effect size of the intervention by Brisson et al. ( 2017 ) is smaller than 0.46 but evidence shows that the effect was sustained for more than 6 weeks. It would have been more interesting if such enduring effect of the interventions is reported by Siegle and McCoach ( 2007 ).

On the other hand, if the focus is to improve university students' mathematics self-efficacy then the intervention (computerized example-based learning equipped with self-efficacy features) by Huang et al. ( 2020 ) is expected to have the highest impact on mathematics self-efficacy. The reported effect size is 0.71 for an intervention that lasted for 1 h. However, the enduring period of the intervention effect size by Huang et al. ( 2020 ) is not reported. Unlike the self-efficacy intervention reported by Luzzo et al. ( 1999 ) which has an effect size of 0.51 with an enduring effect of up to 6 weeks. As such, one may favor the intervention by Luzzo et al. ( 1999 ) over the one by Huang et al. ( 2020 ) if both effect sizes and the enduring effects of the interventions are considered during implementation. It is acknowledged that recommending interventions based on effect sizes could be naïve. The problem here is that different measures of the effect size have been used in different studies and none of them is absolute in the sense that they would allow an absolute comparison of the effectiveness between different studies (Bakker et al., 2019 ; Simpson, 2019 ). Following the line of thought by Simpson ( 2019 ), a higher Cohen's d in Study 1 than in Study 2 does not necessarily mean a larger effect in Study 1 if different measures of mathematics self-efficacy have been used in these studies. Moreover, different intervention studies contained in this review are based on interventions with varying durations which may affect the interpretation of the effect sizes (Bakker et al., 2019 ). It is a challenge to determine, how comparable are effects measured after “a 10-min presentation” or after “3-month intervention”. Finally, if an intervention made in primary schools gives the same effect as another intervention made in tertiary institutions, can one say that the interventions are equally effective? I would interpret that the latter has made a more significant effect because of the more demanding starting point for the intervention. In sum, all the nine interventions are crucial to improving mathematics self-efficacy. This section only provides additional information for those who are interested in the quantification of the impacts of such interventions.

To conclude, mathematics self-efficacy is an important factor that has been widely investigated among researchers on affect in mathematics education research. It plays crucial roles in predicting students' success in mathematics and other cognitive and affect factors. This research reports a systematic review of studies that demonstrate experimentally some strategies to increase students' mathematics self-efficacy. Some effective interventions are identified and described including their underlying mechanisms that foster self-efficacy. Three important groups are identified for the categorization of self-efficacy interventions. First, interventions that directly manipulate sources of mathematics self-efficacy (e.g., Luzzo et al., 1999 ; Cordero et al., 2010 ). For instance, an intervention that provides vicarious experience to students by showing them a video presentation of an older student that narrates her/his experience including coping mechanism while following the same course (Luzzo et al., 1999 ). Second, are the instructional-based interventions. This category of interventions embed some sources of self-efficacy into teaching methods or teacher training programs (e.g., Bonne and Johnston, 2016 ; Kandil and Işiksal-Bostan, 2019 ). An example of interventions in this category is the teaching method that combined mastery experience with social persuasion to foster students' mathematics self-efficacy (Bonne and Johnston, 2016 ). The third theme comprises interventions that embed self-efficacy features in learning activity of the students. For example, the computerized example-based learning with specially designed features for improved self-efficacy is in this category (Huang and Mayer, 2018 ).

Moreover, I also identified two most effective interventions in fostering mathematics self-efficacy. These two interventions are reported by Siegle and McCoach ( 2007 ) for pre-university students and by Huang et al. ( 2020 ) for university students. By implication, the I recommend the reported interventions in these two studies for improved mathematics self-efficacy among pre-university and university students. It is envisaged that if these interventions are implemented the effect will transcend students' convictions about their mathematical capability to improved performance in mathematics. However, one should put into consideration factors such as age and gender of the participants, year of students' study, and cultural diversity of the country. As such, the researcher also recommends replications of these interventions in independent students' populations.

It is crucial to remark that most of the reviewed studies reported the effect sizes of the self-efficacy interventions but very few studies (only four) reported the enduring effect of the respective interventions. This lack of clarity on the enduring effect of mathematics self-efficacy interventions is a limitation of previous intervention studies that future studies should intend to address. The researcher acknowledges that incorporating enduring effect into the design of intervention studies is demanding. Perhaps, the difficulty involved in the design of such studies is the reason why most of the previous intervention studies did not report on the enduring effect. However, a total reliance on the effect size to judge the effectiveness of a self-efficacy intervention is risky. This is because an intervention may have a large immediate effect that fades away soon afterwards. On the contrary, a small immediate effect of an intervention may be sustained for a long time. Therefore, I recommend an adequate attention to the enduring effect in the design of further self-efficacy intervention studies. Further, classroom teachers and instructors may consider using multiple interventions to complement each other such that the students' self-efficacy may improve.

Data availability statement

Author contributions.

YZ: conceptualization, methodology, formal analysis, software, data curation, investigation, visualization, and writing-original draft preparation.

Conflict of interest

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

Publisher's note

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.

Acknowledgments

The researcher acknowledges the support received from the University of Agder library for funding the article processing charge.

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Academic self-efficacy: from educational theory to instructional practice

2012, Perspectives on medical education

Self-efficacy is a personal belief in one&#39;s capability to organize and execute courses of action required to attain designated types of performances. Often described as task-specific self-confidence, self-efficacy has been a key component in theories of motivation and learning in varied contexts. Furthermore, over the last 34 years, educational researchers from diverse fields of inquiry have used the notion of self-efficacy to predict and explain a wide range of human functioning, from athletic skill to academic achievement. This article is not a systematic review of the empirical research on self-efficacy; instead, its purpose is to describe the nature and structure of self-efficacy and provide a brief overview of several instructional implications for medical education. In doing so, this article is meant to encourage medical educators to consider and explicitly address their students&#39; academic self-efficacy beliefs in an effort to provide more engaging and effective instru...

Related Papers

Sevgi Turan

The aim is to identify a relevant framework to study self-efficacy in relation to the impact of medical education curricula. In medical education research, self-efficacy beliefs have mostly been studied in relation to their impact on the mastery of communication competencies and clinical skills. Few studies are available – in the medical domain – that centre on a broader range of medical curriculum competencies, the way self-efficacy improves self-regulated learning, how self-efficacy affects motivation, provides study support, how self-efficacy boosts the career development of students and, how self-efficacy influences social and emotional support of students. © 2012 Published by Elsevier Ltd. Selection and peer review under the responsibility of Prof. Dr. Ferhan Odabaşı

Procedia - Social and Behavioral Sciences

ABSTRACT The aim is to identify a relevant framework to study self-efficacy in relation to the impact of medical education curricula. In medical education research, self-efficacy beliefs have mostly been studied in relation to their impact on the mastery of communication competencies and clinical skills. Few studies are available - in the medical domain - that centre on a broader range of medical curriculum competencies, the way self-efficacy improves self-regulated learning, how self-efficacy affects motivation, provides study support, how self-efficacy boosts the career development of students and, how self-efficacy influences social and emotional support of students. (c) 2013 The Authors. Published by Elsevier Ltd.

Military Medicine

David Cruess

Educação em Revista

Aline Fonseca Franco

ABSTRACT: Self-efficacy is described as an important influencing factor of human behavior, linked to motivation and performance. Thus, its analysis in the educational context is relevant. The study aims to carry out a systematic review of self-efficacy in medical education, nationally and internationally, to analyze the main factors that impact the self-efficacy beliefs of medical professors and students. Therefore, we researched four databases: Virtual Health Library (BVS), Public Medline (PubMed), Brazilian Digital Library of Theses and Dissertations (BDTD), and CAPES Portal, from 2015 to 2020, in Portuguese, Spanish, and English. The descriptors used were: “self-efficacy” and “medicine”, resulting in the selection of 20 studies. Based on the main objectives of the study, we created these categories: 1) self-efficacy and emotional factors, 2) self-efficacy and use of active teaching methodologies, 3) student self-efficacy and different teaching methods, 4) self-efficacy, motivatio...

International Journal Of Community Medicine And Public Health

Anshuman Sharma

Background: In present scenario academic self-efficacy is an important key factor to assess academic progress among students, so that their outcome in exams can be enhanced. The aim of this study was to assess academic self-efficacy among medical students according to present curriculum.Methods: This was a cross sectional study conducted among 120 students of Shyam Shah Medical College, Rewa (MP). Samples were selected from exam going students of third professional examination, either current batch or detained batch. A self-administrated, structured questionnaire was developed to collect data from the undergraduates. Assessment according to objective questions from their current syllabus was done to assess academic self-efficacy, the study adapted the questions framed by faculty members of third professional students. 200 questions from all the subjects of third year were included. Data were collected and data analysis was done by applying proper statistical tests.Results: The mean ...

Anatomical Sciences Education

Jennifer Burgoon

American Journal of Educational Research

Bunmi Malau-Aduli

Suez Canal University Medical Journal

Medical Education Department

Abdul Sattar Khan

Objective: To determine the association of perceived self-efficacy with academic performance of pre-clinical medical students. Study Design: A cross-sectional analytical study. Place and Duration of Study: Medical Education Department, Ataturk University, Turkey, from March to May 2012. Methodology: Participating students were members of the first to third year medical students class considered to be preclinical years at Ataturk University. A validated and reliable questionnaire consisted of 10 questions applied to assess the general self-efficacy of the medical students in pre-clinical years and evaluate whether their self-efficacy has relation to their academic performance. Responses and studied variables were compared using ANOVA and Pearson correlation test as applicable. Results: The mean scores of three consecutive examinations were compared with self-efficacy mean scores of three classes. A validated and reliable questionnaire was used for assessment of self-efficacy. There w...

Anatomical self-efficacy is defined as an individual&#39;s judgment of his or her ability to successfully complete tasks such as dissecting, learning anatomical knowledge, and applying anatomical knowledge to clinical situations. This research investigates medical student self-efficacy for the anatomy curriculum. Five surveys containing the same embedded anatomical self-efficacy instrument were completed by first-year medical students at the University of North Carolina School of Medicine; one pre-survey administered prior to students beginning a medial gross anatomy course and four post-surveys administered after students completed examinations during the course. Additional data collected included anatomical experiences prior to medical school, demographic information, MCAT scores, and anatomy exam scores, both written and laboratory practical. The results of the study indicated that when controlling for academic ability, the quantity of anatomical experiences prior to medical scho...

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