The self-efficacy and academic performance reciprocal relationship: the influence of task difficulty and baseline achievement on learner trajectory

ABSTRACT

According to Bandura’s theory of reciprocal determinism, self-efficacy and academic achievement can have a mutual influence over one another. While empirical research generally supports this position, little focus has been given to within- and between-person factors that may moderate this relationship. The present study explored how initial performance and task difficulty impact learners’ performance and self-efficacy trajectory over subsequent tasks. A diverse university sample of 261 students; 118 females aged 18–67 and 143 males, aged 18–64, was used. Significant positive pathways were found in the achievement-self-efficacy direction, but not for pathways in the self-efficacy-performance direction, failing to support the reciprocal hypothesis. Repeated measures ANOVA also revealed a moderating influence of initial task performance on the interaction between task difficulty and achievement over time. The findings highlight the importance of enhancing academic performance through scaffolded mastery, particularly for those with initial low performance outcomes, to build self-efficacy for learnt skills.

KEYWORDS:

• Self-efficacy
• academic achievement
• reciprocal effects
• task difficulty
• learning trajectory

Understanding the determinants of academic achievement in higher education contexts has been a significant focus of research for several decades (Richardson et al., 2012; Robbins et al, 2004; Schneider & Preckel, 2017). Among these determinants, self-efficacy has consistently emerged as a highly influential motivational variable (Honicke & Broadbent, 2016; Richardson et al., 2012). Whilst empirical findings provide ongoing support for this relationship, this is largely reflected in findings that are correlational and cross-sectional by research design. Resultingly, longitudinal interactions, particularly as they pertain to reciprocal influences between self-efficacy and achievement have been less considered. Despite this, the emerging body of research exploring the reciprocal influence between self-efficacy and achievement generally supports this relationship (Talsma et al., 2018). Research in this area now needs to build on the significant findings from reciprocal studies and explore the nuances in the self-efficacy-achievement relationship that can enhance the understanding of their interaction. The current study aims to investigate such reciprocal relationships between self-efficacy and academic achievement and further consider between- and within-person factors that may be involved in moderating this relationship.

Self-efficacy refers to an individual’s judgement of their capabilities to execute a course of action required to achieve desired performance (Bandura, 1997). As a key driver of achievement motivation, self-efficacy regulates the perception of difficulty and subsequently the amount of effort and persistence given to completing a task (Bandura, 1978, 1997). Self-efficacious students are more likely to set higher goals for themselves, exert more effort and perseverance in the pursuit of goals despite difficulty or setbacks, and tend to academically outperform peers with lower self-efficacy (Honicke & Broadbent, 2016).

Decades of research highlight the positive effects of self-efficacy on academic achievement (Schneider & Preckel, 2017). However, the influence of academic performance on self-efficacy is often less considered. One exception is Bandura’s (1978) theory of Reciprocal Determinism, which outlines how personal, environmental and behavioural factors mutually interact and influence each other. Reciprocal determinism accounts for the influence of self-efficacy on achievement but also considers how academic success influences the learner’s confidence about future tasks. When a student has high self-efficacy, this interacts with positive academic behaviours (e.g., perseverance when experiencing setbacks) to influence environmental outcomes (e.g., academic success). In turn, this successful learning experience acts as a source of mastery that further influences future behaviours and levels of self-efficacy. When the experience of success in a specific task builds confidence in the learner’s ability to achieve in such tasks in the future, this is known as mastery of experience (Bandura, 1997). This factor is considered the most powerful source of self-efficacy and explains how academic performance influences subsequent self-efficacy levels.

Despite the theoretical and common-sense nature of this proposed reciprocal relationship, it has seldom been directly tested in the extant literature. Overwhelmingly, much of the support for the self-efficacy-achievement relationship has come from cross-sectional research findings. As such, the temporal patterns of influence to establish reciprocity cannot be investigated. Recently, studies have shown increasing interest in using longitudinal research approaches to better understand the relationship between self-efficacy and performance. Such studies have highlighted influences in (1) academic performance → self-efficacy, (2) self-efficacy → academic performance, or (3) reciprocal relationships between these variables. However, the strength of the direction of these relationships have been mixed.

Longitudinally, academic performance has consistently and positively predicted subsequent levels of self-efficacy. Such findings are robust across age/education level, testing in artificial (Richard et al., 2006) and ecologically valid learning environments (Gibbons & Raker, 2018; Schober et al., 2018), as well as in short and long lag time measurement waves, however shorter lag times show stronger predictive effects (Talsma et al., 2018). Interestingly, despite this relationship remaining significant over time, some studies taking multiple performance → self-efficacy measures report the strength of the relationship somewhat decreases over time (Gibbons & Raker, 2018; Villafane et al., 2016). Overall, learners with a history of strong academic performance tend to report higher levels of self-efficacy. This demonstrates the potent influence of mastery of experience, via academic performance, in shaping self-efficacy beliefs over time.

Findings have been less consistent regarding the predictive utility of self-efficacy on performance in longitudinal research situations. A significant body of literature supports a relationship in the self-efficacy → performance direction, regardless of whether prior performance is considered, although the relative strength of this relationship is consistently less than that in the performance → self-efficacy direction (Talsma et al., 2018; Valentine et al., 2004). Yet, some researchers claim self-efficacy holds no unique influence over subsequent performance, and is rather, a reflection of past performance and its effect on future performance rather than the effects of self-efficacy (Richard et al., 2006; Sitzmann & Yeo, 2013; Vancouver & Kendall, 2006; Vancouver et al., 2002).

Lastly, a handful of studies have tested reciprocal relationships between self-efficacy and academic achievement utilising cross-lagged panel models (CLPM) or path analytic approaches (Burns et al., 2019; Caprara et al., 2011; Gibbons & Raker, 2018; Hwang et al., 2016; Schober et al., 2018 Villafane et al., 2016). Most have reported significant reciprocal effects (exc. Gibbons & Raker, 2018; Schober et al., 2018). Despite evidence of a bidirectional relationship, less consideration has been given to the specific conditions that enable self-efficacy and performance to enact effects on each other which may account for mixed findings.

Contextual factors, such as task difficulty and similarity across time may offer further insight on the reciprocal relationship. Such factors are difficult to account for in studies situated in pre-existing learning environments (i.e., schools or university) where performance tasks are already developed and embedded into learning programmes. Previous research findings suggest a negative relationship between perception of task difficulty and self-efficacy, whereby high levels of self-efficacy result in lower evaluations of task difficulty (Lee & List, 2021). Given this, performance tasks that are too simple are likely to overinflate levels of efficacy for those on the lower end of the performance success continuum and disengage those with high levels of self-efficacy and performance potential, who do not feel adequately challenged. Counter to this, highly difficult tasks do not provide sufficient opportunity for mastery across all levels of the performance continuum (Power et al., 2020). Given the extensive empirical support for the potent impact of mastery of experience on enhancing self-efficacy, via prior achievement success, an exploration of how these factors may influence the reciprocal relationship may be enlightening.

To our knowledge no study has tested the reciprocal relationship between self-efficacy and performance (1) using an experimental framework which controls task difficulty with a measure designed to have comparative levels of progressive difficulty across each performance timepoint, (2) by conducting additional analysis of the path model that explores the interaction between levels of task difficulty and performance across timepoints, and (3) by specifically observing the learning trajectory of initial low performing individuals to determine if subsequent improved performances can offset initial poorer performance.

We contribute to existing findings (e.g., Burns et al., 2019; Caprara et al., 2011; Gibbons & Raker, 2018; Hwang et al., 2016; Schober et al., 2018; Villafane et al., 2016) by first investigating specific reciprocal relationships between self-efficacy and performance and explore the magnitude of this relationship. Secondly, we explore the degree to which an individual’s initial performance level on a challengingly balanced task is a determinant of immediate measures of self-efficacy and future performances on similar challengingly balanced tasks. Such focus can elucidate the value of cumulative effects of self-efficacy and performance’s influence on each other over time and highlight how previous performance situations influence the trajectory of comparative future performance outcomes. To this effect, we are specifically interested in understanding how initial poor performance on a task may interact with immediate self-efficacy levels to influence future self-efficacy and performance levels, and whether increased performance on subsequent tasks can negate the effects of initial poor performance to manipulate a learner’s achievement trajectory.

Aligned with the theoretical position relating to reciprocal determinism, nested within Bandura’s Social Cognitive Theory (Bandura, 2005), and enhanced by previous research findings on self-efficacy and academic achievement, we present three hypotheses for investigation: (1) Self-efficacy will predict academic performance; whereby high levels of self-efficacy will result in higher levels of performance, and vice versa for the predictive effect of academic performance on self-efficacy. (2) A positive reciprocal relationship exists between self-efficacy and academic achievement; whereby high levels of self-efficacy will produce subsequent high levels of academic success and vice versa across time. (3) A learner’s initial level of academic performance will moderate subsequent performance success based on task difficulty, whereby lower levels of initial performance will result in consistently lower levels of subsequent achievement in challenging learning tasks, compared to those with initial high performance, who will maintain higher subsequent levels of achievement in challenging learning tasks.

Method

Participants

The sample included 261 participants, 45.2% (n = 118) identifying as female were aged 18–67 years, (M = 24.4, SD = 5.75) and 54.8% (n = 143) identifying as male aged 18–64 (M = 23, SD = 4.93). Participants had to be over the age of 18 and enrolled in a course (also known as a program) at university. As participants were recruited using Prolific (a platform for research recruitment https://www.prolific.co), participants could be undertaking their university studies anywhere in the world, with the majority located in Western and Northern Europe (46%), followed by the United Kingdom and Ireland (25%), Eastern and Central Europe (20%), The Americas (6.5%), Australia (1.5%) and Western Asia (<1%). A total of 27 countries were represented in the sample. Participant study discipline varied widely, with 29.5% studying a Science discipline, 20.6% studying in Arts and 14.1% studying in both Health and IT. The majority of participants were enrolled in undergraduate programmes (67.7%; n = 176), followed by postgraduate studies (32.3%; n = 84), with one participant not disclosing. The subjective social status (Adler et al., 2000), used as a proxy for Socio-Economic Status of participants, was also reported (M = 5.72, SD = 1.63), and indicates self-reported SES of participants in the middle of the scale.

Materials

Demographic information

Demographic information such as age, gender, subject area of tertiary study, and degree type, as well as subjective social status was collected from participants.

Self-efficacy

The measure of self-efficacy at Time 2 (T2 self-efficacy) and Time 4 (T4 self-efficacy) were adapted from Bandura’s problem-solving self-efficacy scale in the guide to constructing self-efficacy scales (Bandura, 2005). Level of confidence was rated between zero and 100. Zero to 100 was chosen because self-efficacy scales with few intervals are less sensitive and reliable at detecting effects (Pajares et al., 2001). An example question is ‘I can solve 1 out of 10 of the problems’. The scale was found to have a high level of internal consistency (α = .96). Higher scores indicate greater levels of self-efficacy.

Performance

As students came from various countries, educational systems and disciplines, we chose a performance task widely known for its cultural sensitivity and low relatedness to subject-specific content that you might assume participants are being exposed to within degrees. Performance success was measured through use of abstract reasoning problems adapted from Raven’s standard 60-item progressive matrices and Raven’s 48-item advanced progressive matrices (Raven, 1981). Each problem contained a 3 × 3 grid of abstract patterns, with the final pattern missing from the grid. There were four options given as a potential correct response for the missing item in the 3 × 3 grid. See Figure 1.

Figure 1. Example abstract problem used as a measure of performance.

Performance at time 1 (T1 Performance): consisted of two sample questions – one ‘easy’ and one ‘hard’ question. See test standardisation below in procedure for elaboration. Performance at time 3 and 5 (T3 and T5 Performance): consisted of ten abstract reasoning problems at each timepoint. The problems selected for each timepoint set were of comparable difficulty, where five were considered easier problems to solve and five were considered more difficult problems to solve. See test standardisation below in procedure for elaboration.

Procedure

Following University Ethics Committee approval for this study, items with a range of difficulty were selected from Raven’s standard 60-item progressive matrices and Raven’s 48-item advanced progressive matrices (Raven, 1981). They were then adapted for use in the current study.

Test standardisation

Test standardisation occurred on an additional and separate group of twenty-eight participants (who were all over the age of 18) through three steps before different participants (as listed in the participant section) performed the current study. First, the difficulty of the abstract reasoning items selected for the performance timepoints was initially determined using the Raven’s matrices test manual normed scores. From this, Time 1 performance items were selected, requiring one ‘easy’ and one ‘hard’ item. An ‘easy’ item had a greater than 90% correct response rate, and a ‘difficult’ item had a less than 30% correct response rate, according to Raven’s test manual normed scores. Second, an additional 28 of the adapted abstract reasoning problem items, created by the research team, were tested for difficulty in a small standardisation sample of participants (n = 28). Item difficulty was determined by the percentage of participants who scored the item correctly. Third, the 28 items were then ranked from the highest correct response rate to the lowest correct response rate. Given Time 3 and 5 performances required 10 items each, 20 items from the 28 items used in the standardisation trial were able to be matched in pairs according to rank difficulty from easiest to hardest. One item from each pair was allocated to Time 3 and Time 5 performance sets. This was to ensure that performance across time was as equivalently matched as possible. These were also cross-referenced with Raven’s matrices test manual normed scores to create two performance sets (T3 and T5 performance) with 10 abstract reasoning problems in each set. Performance was scored out of 10 within each set.

Participant recruitment and study procedure

Once final item sets had been determined, recruitment for the study was conducted using the online research recruitment site Prolific. A repeated measures approach was adopted to collect the sequence of data related to performance and self-efficacy. After providing consent, demographic information was collected, and the practice rating task (T1 performance) was completed by participants. Within practice, participants completed an ‘easy’ and a ‘difficult’ abstract reasoning problem and were given feedback on their performance, receiving a score out of two. Participants were then asked to rate their confidence in completing similar tasks (T2 Self-Efficacy). Participants then completed the abstract reasoning task again (T3 Performance), this time with 10 abstract reasoning problems and received a score out of ten. Participants then completed the measure of self-efficacy for a second time (T4 Self-Efficacy) prior to completing another 10 abstract reasoning problems for the last time (T5 Performance; see Figure 2). The data was collected using the online survey tool Qualtrics (https://www.qualtrics.com/au/). Participants were given a maximum of 30 min to complete the task and had to complete the task in one sitting. Participants were paid $5AUD to remunerate them for their time commitment to the study.

Figure 2. Temporal sequence of repeated measures timepoints for performance and self-efficacy measures.

Data was analyzed using a combination of SPSS for testing hypothesis 1 and 3 (Correlational and ANOVA analysis) and AMOS for testing hypothesis 2 (Reciprocal relationship between self-efficacy and performance).

Results

Preliminary data cleaning and assumptions testing

The final sample consisted of 261 participants, after removing 56 participants who had a high percentage of missing data. Following deletion of these, MVA revealed <1% missing data. Results of Little’s MCAR test suggested a random pattern of missingness, χ² = 40.28, df = 40, p = .46 and so expectation maximisation was performed as a method for replacement (Tabachnick & Fidell, 2013). A manual screen of the replaced data found all values were within the possible range of scores. Multivariate normality was assessed using Mardia’s test in AMOS. This value (2.27) was less than Byrne’s (2010) suggested cut-off value of 5, meeting the multivariate normality assumption. Paired samples t-tests revealed statistically significant differences between scores for T2 and T4 self-efficacy, t(260) = −12.98, p < .01, and T3 and T5 performance t(260) = −2.33, p = .021, suggesting sufficient variance exists to model longitudinal relationships using path analysis.

Descriptive statistics

Means, standard deviations and score range for all timepoints of self-efficacy and performance are presented in Table 1, with zero-order bivariate correlations for performance and self-efficacy scales presented in Table 2. At all timepoints, participants performed moderately and reported feeling a medium level of confidence. While achievement levels were similar across timepoints, participants reported feeling more confident at Time 4 compared to Time 2. All values of absolute skew and kurtosis were within the range suggested by Field (2013), indicating data normality.

Table 1. Mean scores, standard deviations, range and normality statistics of self-efficacy and performance scales.

	M	SD	Range	Min/Max	Skewness	Kurtosis
T2 Self-efficacy	52.82	20.99	0–100	0/100	−.432	−.093
T4 Self-efficacy	65.58	18.01	0–100	0/100	−.726	1.153
T1 Performance	0.46	0.54	0–2	0/2	.551	−.929
T3 Performance	7.23	1.66	0–10	2/10	−.437	−.224
T5 Performance	7.55	2.11	0–10	1/10	−.843	−.054

T1 = Timepoint 1; T2 = Timepoint 2; T3 = Timepoint 3; T4 = Timepoint 4; T5 = Timepoint 5.

Table 2. Correlation matrix of self-efficacy and performance scales.

	T1 Performance	T2 Self-efficacy	T3 Performance	T4 Self-Efficacy	T5 Performance
T1 Performance	1
T2 Self-efficacy	.38**	1
T3 Performance	.17**	.16*	1
T4 Self-efficacy	.21**	.68**	.47**	1
T5 Performance	.16*	.22**	.55**	.31**

Note. ** p < .01 (2-tailed); * p < .05 (2-tailed); T1 = Timepoint 1; T2 = Timepoint 2; T3 = Timepoint 3; T4 = Timepoint 4; T5 = Timepoint 5.

Hypothesis 1. Correlations between self-efficacy and performance

The correlation analysis (Table 2) indicates significant positive correlations among all included variables. Moderate positive correlations were observed between T2 self-efficacy and performance at T1, and self-efficacy at T4 and T3 performance. It is noted that the strongest observed correlations were those paths connecting scales of self-efficacy with each other and performance with each other. All remaining correlations, although reaching significance are small.

Hypothesis 2. Reciprocal relationship between self-efficacy and performance

Overview. The statistical program AMOS v26 (Arbuckle, 2014), designed as a tool specifically for path analyses, was used to test the hypothesis that self-efficacy and academic performance reciprocally influence each other (H2). Model fit was measured using relative fit indexes; Comparative Fit Index (CFI) and Tucker Lewis Index (TLI), with good fit indicated in values > .95 (Hu & Bentler, 1999). Absolute index values were used, including chi-square/df (good fit < 3), and Root Mean Square Error of Approximation (RMSEA; good fit < .06, acceptable fit < .08; Hu & Bentler, 1999).

Path models. The hypothesised model (Figure 3) shows the path coefficients as standardised regression weights and the predicted temporal and reciprocal influences of task performance and self-efficacy. Relative fit indexes demonstrated good fit, however absolute indexes showed poor data fit (χ² (3) = 11.706, p = .008; RMSEA = .106; CFI = .977; TLI = .924).

Figure 3. Hypothesised model showing temporal relationships between performance and self-efficacy. Note. ** p < .01, * p < .05.

Modification index values suggested addition of paths between T1 performance and T4 self-efficacy and T2 self-efficacy to T5 performance. Considering past research and to optimise model fit to the data, re-specification was made to include suggested associations, given they are theoretically justified (Figure 4). The re-specified model showed excellent fit, with this model being superior to the hypothesised model (χ² (1) = 0.53, p = 0.819; RMSEA = .000; CFI = 1; TLI = 1).

Figure 4. Re-specified model showing temporal relationships between performance and self-efficacy. Note. ** p < .01, * p < .05.

The final model revealed significant direct effects in all paths, except for paths from T2 self-efficacy → T3 performance (p = .107), T1 performance → T3 performance (p = .085) and T4 self-efficacy → T5 performance (p = .392). All pathways representing predictions from a performance measure to an self-efficacy measure were significant, whereas pathways predicting performance from measures of self-efficacy, except for T2 self-efficacy → T5 performance, did not reach significance. Additionally, several significant indirect effects of Time 1 performance were also observed. Performance at Time 1 exerted an indirect effect on self-efficacy at Time 4 via Time 2 self-efficacy (β = .24, p = .002), and Time 5 performance via Time 3 performance (β = .07, p = .003).

Hypothesis 3. Moderating influence of initial performance level

Overview. To further explore path analysis findings, within-group trends in changes to self-efficacy and performance levels over time were observed by generating spaghetti plots using RStudio v1 (see Supplementary file). A repeated measures ANOVA was conducted in SPSS v28 to investigate potential influences of initial performance level on participants’ performance trajectory.

First, data were split based on participant performance in trial questions (T1 performance), with those who did not correctly answer any trial questions categorised as low initial performers, and those who correctly answered one or both trial questions categorised as high initial performers. A one-way ANOVA found a statistically significant difference in T2 self-efficacy levels for those who scored either zero, one or two from a possible score of two for T1 performance. Post Hoc probing revealed significantly lower levels of T2 self-efficacy for those who scored zero in T1 performance (M = 45.88, SD = 20.68) compared to scores of one (M = 61.49, SD = 18.03) or two (M = 67.90, SD = 10.94). There was no significant difference in levels of T2 self-efficacy for those who scored one or two for T1 performance. Those with a zero score at T1 performance reported significantly lower levels of T2 self-efficacy than those with a score of one or two. This formed the basis of an adopted two group approach for initial levels of performance.

ANOVA

To test hypothesis three, a 2 (Initial performance: high, low) × 2 (Performance timepoint: T3/T5) × 2 (Item Difficulty: easy, hard) repeated measures ANOVA was conducted, with initial performance as a between group and item difficulty and performance timepoint as within group effects. Recall from the test standardisation sub-section within the procedure that item difficulty was pre-determined for the two performance sets prior to the study by assigning five items with the highest correct response rate from normed data as ‘easy’ and five items with the lowest correct response rate from normed data as ‘hard’. This produced four combinations of difficulty; set 1 easy, set 1 hard, set 2 easy and set 2 hard. See Table 3 for mean performance scores for item difficulty sets (scored out of five) based on initial performance level.

Table 3. Mean scores and standard deviations of item difficulty performance compared by initial performance level.

	Item difficulty	Initial performance level	M	SD
1	T3 Performance – Easy	High	4.19	0.76
		Low	4.15	0.88
2	T3 Performance – Hard	High	3.40	1.26
		Low	2.90	1.19
3	T5 Performance – Easy	High	4.15	1.04
		Low	3.71	1.10
4	T5 Performance – Hard	High	3.79	1.34
		Low	3.54	1.36

The following main effects were significant: Performance timepoint, (F(1, 259) = 5.679, p < .05, ${\eta }_p^2$= .02), and Item difficulty, (F(1, 259) = 114.805, p < .001, ${\eta }_p^2$= .31). Significant interaction effects were also observed in: Performance timepoint × Item difficulty, (F(1, 259) = 50.732, p < .001, ${\eta }_p^2$= .16), and Performance timepoint × Item difficulty × Initial performance, (F(1, 259) = 9.323, p < .01, ${\eta }_p^2$= .04). This significant 3-way interaction suggests item difficulty and performance timepoint interact differently for those with initial low or high-performance levels. Given non-significant 2-way interactions of initial performance with performance timepoint and item difficulty, the data were split based on initial performance to separate the influence of high or low initial performance on interactions between performance timepoint and item difficulty.

Interaction probing revealed significant interactions between performance timepoint and item difficulty at high, (F(1, 113) = 8.882, p < .01, ${\eta }_p^2$= .07) and low (F(1, 146) = 52.289, p < .001, ${\eta }_p^2$= .26) initial performance levels, with larger effects observed for those in the low group. See Figures 5 and 6 for simple slopes.

Figure 5. Interaction of item difficulty and performance timepoint for low initial performance.

Figure 6. Interaction of item difficulty and performance timepoint for high initial performance.

Discussion

Research supports a reciprocal relationship between self-efficacy and achievement. However, much of this is in real learning contexts, with differences in content and difficulty of performance measures across timepoints. This confounds skill transfer across tasks, influencing task mastery development, subsequently impacting self-efficacy levels and the magnitude of reciprocal effects over time. Additionally, prior studies analyse reciprocal trends without considering baseline levels of learner performance and observing how self-efficacy and performance trajectory may be uniquely influenced by this. Thus, the current study explored the nature of specific reciprocal relationships between self-efficacy and achievement, while controlling task difficulty and similarity, and exploring how differences in baseline levels of performance influence subsequent levels of self-efficacy and performance. The current study presented three primary hypotheses. Partial support was found for hypothesis 1, whereby performance predicted subsequent self-efficacy, but not vice versa. Subsequently, no support was found for reciprocal relationships between these variables (Hypothesis 2). Finally, support was found for a moderating influence of initial level of performance on future performance timepoints, with an additional significant interaction of task difficulty (Hypothesis 3).

The influence of academic achievement on self-efficacy

The strongest predictive model pathways found were for positive relationships between participant achievement and subsequent self-efficacy. These findings are consistent with self-efficacy theory related to mastery of experience (Bandura, 1997), and prior longitudinal research in tertiary (Burns et al., 2019; Gibbons & Raker, 2018; Villafane et al., 2016) and meta-analytic studies (Talsma et al., 2018). This shows students, at least to some degree, rely on successful past performance to develop self-efficacy beliefs and continue reliance on successive performance outcomes to maintain or enhance these beliefs. This was the case in all performance → self-efficacy pathways except for T1 performance → T4 self-efficacy, which showed a weak, but significant, direct negative effect. This is not surprising given the tendency for judgements of efficacy to become stronger and more accurately calibrated to task performance as it is repeated and completed successfully (Shea & Howell, 2000). In the context of the current study, consistent increases to levels of self-efficacy and performance were observed over time, for those with high and low levels of initial performance. Consequently, lower performance levels at baseline are likely to be less predictive of the most distal future measures of self-efficacy because they have been influenced by more recent performance success.

Interestingly, the strength of the significant achievement → self-efficacy relationships were greater and more consistent across timepoints than previously reported studies testing reciprocal effects. While this contradicts findings in which the strength of this relationship appeared to decrease over time (Burns et al., 2019; Gibbons & Raker, 2018), it should be noted that previous studies used intervals of weeks/months which may explain differing findings. Further, as task content and difficulty were controlled across performance in the current study, participants may have made more accurate self-efficacious judgements. Our findings suggest that temporal proximity, coupled with consistency in tasks measuring performance over time may increase cumulative effects of achievement on self-efficacy levels. This makes sense, in the context of self-efficacy theory, given the opportunity for mastery through experience would be affected by different performance task types. In turn, this would directly impact the accuracy of subsequent efficacious beliefs.

Further, worthy of discussion are differences in task performance and subsequent reported self-efficacy for individuals with high and low initial performance. Our study shows a tendency for initial high performers to sustain higher performance levels, for both easy and difficult task items, and high self-efficacy levels across timepoints. This embodies the quintessential influence of successful mastery experience in promoting ongoing high levels of self-efficacy. However, a surprising trend emerged among initial low performers who experienced moderate task success in subsequent performance trials. For these individuals, marked increases in self-efficacy from T3 to T5 were observed in addition to increased performance, particularly for difficult items over time. This reflects a kind of rebound effect, whereby more successful task performance following failure recalibrates the trajectory of future performance and judgements of efficacy. Further, this effect appears strongest when increased success is experienced for more difficult task items.

The influence of self-efficacy on academic achievement

Significant pathways that hypothesised self-efficacy to predict future achievement were not found, except for the positive and direct pathway between T2 self-efficacy and T5 performance. Our findings largely suggest confidence levels had no significant influence on future achievement, which is largely discordant, though not novel, among previous research focusing on their reciprocal relationship (Burns et al., 2019; Caprara et al., 2011; Gibbons & Raker, 2018; Schober et al., 2018; Villafane et al., 2016). Interestingly, all three self-efficacy → performance paths in the model differed in levels of significance and direction of predictive effects. This suggests that, in our study at least, the substantive mechanisms that enable self-efficacy to enact its effect on performance may vary across timepoints and have differing levels of influence over time. We consider three possibilities to explain this.

Firstly, it is possible that the initial (two item) novel performance task was inadequate exposure to garner mastery and reduce task ambiguity enough for subsequent measures of self-efficacy to reliably predict future performance. This is plausible for three reasons; (1) in this study T1 performance did not directly predict T3 performance and although significantly predicting T2 self-efficacy, self-efficacy was not a predictor of subsequent T3 performance, (2) efficacious judgements are suggested to be cumulative and become stronger and more predictive of performance over time as a task is repeated (Gore, 2006) and (3) there is evidence to suggest that self-efficacy’s impact on performance may be influenced by experimental studies that are limited to short time exposure to learning opportunities or trials (Beattie et al., 2014). Such is the case in the present study, where the number of learning opportunities was no greater than ten items in each performance trial. Where levels of self-efficacy are not fully realised, the ability to accurately predict success in future performance is likely to be hindered.

Secondly, the significant effect of the more distal measure of T2 self-efficacy on T5 performance compared with the non-significant pathway from T4 self-efficacy to T5 performance is unusual and deviates from past findings. Longitudinal literature has found the closer the proximity of the self-efficacy – performance measurement timepoint, the stronger the predictive effect (Galyon et al., 2012; Gore, 2006). The discordant findings in this study may be the result of the interaction of initial performance and subsequent performance and efficacy measures taken over time. Interestingly, for low initial performers, mean self-efficacy levels and performance change disproportionately over time, with larger increases in T2 to T4 self-efficacy observed than increases in performance from T3 to T5. This trend is not generally observed in initial high performers, who tend to maintain levels of self-efficacy and performance (for both easy and difficulty items) throughout trials. When taken alongside a decrease in achievement score for easy items from T3 to T5 performance for low initial performers and an increase in score for hard items, overconfidence may have impacted T5 performance. The contrasted increase in performance score at T3, compared to the initial zero score, may have over inflated self-efficacy, leading to overconfidence for the next performance task, where overall achievement stagnated. Where individuals are overconfident, they may be less likely to expend effort, or become complacent on tasks that are considered easy resulting in reduced achievement for easy items (Richard et al., 2006 Vancouver et al., 2002). This aptly reflects a Dunning-Kruger effect in those with initial low performance, who appear to overestimate their capabilities for future performance success (Dunning, 2011). Conversely, low initial performer’s high levels of self-efficacy when confronted with more challenging items may have mobilised enhanced effort and persistence for success, resulting in greater achievement for hard items. There appears to be a duality to enhanced levels of self-efficacy for initial low performers after experiencing some success, and this is dependent on the difficulty of the task.

Thirdly, overall participant motivation for successful task completion may have been low. Given the artificial nature of the achievement task, it is possible this may have produced insufficient motivation or task value to expend the effort required for success. Self-efficacy exists in a complex wider framework of known motivational and cognitive factors that interact to influence achievement (Pintrich, 2000). In these frameworks, several achievement-related determinants influence self-efficacy, like task interest and value, effort regulation (sustained effort and persistence despite difficulty), and goal orientation (achievement outcome individuals strive for that motivates engagement in learning). Such variables are shown to influence achievement (Honicke & Broadbent, 2016; Richardson et al., 2012) and even exist in mediating relationships with self-efficacy and achievement (Galla et al., 2014; Honicke et al., 2019). If participants had no genuine stake in the outcome of the task, the motivation to achieve, the degree of effortful engagement and the achievement outcome goal they set for themselves would all be lower, despite high levels of self-efficacy. If this is so, this highlights the importance in considering the influence of such variables when investigating future reciprocal effects.

Prior findings have reported positive predictive effects of self-efficacy on future performance (Burns et al., 2019; Villafane et al., 2016), negative effects (Vancouver et al., 2002), or no effect at all (Gibbons & Raker, 2018). This indicates findings from longitudinal research are uncovering significantly more complex interactions between self-efficacy and performance than previously considered in cross-sectional research settings. The current findings provide more context to this relationship and highlight that self-efficacy’s direct influence on future performance can be a myriad of positive, negative, and null effects at different points in time within the context of the same learning experience. This presents future research opportunities that focus on longitudinal interactions between self-efficacy, performance and contextual, motivational, or cognitive conditions of the learner.

Limitations and future research implications

In interpreting the findings of this study, several limitations should be considered, which highlight possible directions for future research. It is feasible that time lags between measures of self-efficacy and achievement in the current study were too short for self-efficacy beliefs to sufficiently develop. While findings have reported shorter time lags producing stronger reciprocal effects between self-efficacy and achievement (see Talsma et al., 2018), the shortness of these lags were in weeks, or learning semesters, and not in minutes, as reflected in the current study. It may be that a ‘sweet spot’ which measures of self-efficacy and achievement optimally influence each other exists, and that shorter (less than a few weeks) and longer (greater than a year) lags dampen existing reciprocal effects.

Secondly, while experimental approaches are considered ‘gold standard’ and allow control for relevant extraneous effects, in the case of the current study, task artificiality may have been created. As mentioned earlier, this may have impacted on other unobserved cognitive and motivational variables influential on achievement, most relevantly task value and effort regulation. Given the complex interaction of mental processes that occur within learning contexts, that can be difficult to authentically replicate, perhaps greater flexibility is needed in research approaches. A hybrid of quasi-experimental and observational approaches to tackling causality in educational research may be a ‘the best fit’ approach to juggling the issue of internal validity and findings having practical value. This is likely to involve more longitudinal studies that investigate the intricacy of the performance ↔ self-efficacy relationship being conducted within the context of single subjects.

Practical implications and conclusion

The present findings highlight that programs and strategies that target self-efficacy directly may not produce the greatest measurable increases in student learning. This does not discredit the usefulness of such strategies, given greater self-efficacy will likely enhance other positive learning behaviours that enhance achievement. Rather, holistic approaches which also consider the impact of prior learning success may produce greater overall effects in achievement situations. Further it may be more beneficial to first target and foster initial performance success and scaffold such success over time, to build self-efficacy specifically for the skills being learnt. This appears particularly important for those with low performance outcomes after first exposure to new information or skills. Through this mastery of experience self-efficacy is likely enhanced, which subsequently mobilises other positive learning behaviours and processes that contribute to future learning success. Indeed, this approach is more likely to be used consistently in early years learning environments, such as primary and high school, but may be less common in the tertiary setting. This is owing to varied constraints that exist within tertiary organisations, including large enrolment sizes, increased learner demographic diversity and time that may not be available to implement teaching and learning practices of this nature.

Despite a lack of support for longitudinal reciprocal effects between self-efficacy and achievement in this study, further research should still focus on understanding the reciprocal interaction between them. Characteristics of the learner and their environment, such as task value, difficulty and genuine opportunity for mastery need more conscious consideration to diversify research findings and understand the self-efficacy-achievement interaction across a range of learning contexts. In current tertiary learning settings, educators should consider strategies that incrementally build achievement success. This includes scaffolding task difficulty and offering multiple exposures of comparative learning content. Identification of academically at-risk students should occur at the earliest signs of low performance, or failure, with support that harnesses the powerful effect of mastery of experience on enhancing efficacious beliefs, as well as the positive effect that past achievement success has on enhancing future achievement success.

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors have no competing interests to declare that are relevant to the content of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).