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Abstract

Engagement in learning activities is crucial for academic success in higher education. However, many students engage in learning activities less frequently than initially intended. This results in an intention–behavior gap. Drawing from theories of action regulation and motivation, the present study examines intentions in the adaptation phase at the beginning of an academic term and volitional competencies (VOCO) as predictors of the intention–behavior gap. A sample of N = 675 preservice teachers from five cohorts under regular and under pandemic conditions participated in the longitudinal study over one academic term. We assessed intentions and behavior regarding six learning activities. Results indicated lower intentions in the middle of the term than in the beginning. Furthermore, we observed an intention-behavior gap. Regression analyses revealed that higher VOCO predicted a greater use of learning activities. However, they failed to predict the size of the intention–behavior gap. The findings highlight the relevance of psychology educators supporting students’ engagement, especially during the critical early stages of the academic term. Future research should explore other factors to explain individual differences in the intention–behavior gap.

Keywords

Intention–behavior gap academic engagement volitional competencies preservice teachers

Introduction

Learning situations in higher education are characterized by a high degree of autonomy (e.g., Bellhäuser et al., 2016; Bernardo et al., 2019). Most courses provide minimal regulation regarding the quantity and quality with which students approach the content during an academic term. As a result, learning in higher education is largely self-regulated, meaning that students take learning-related decisions themselves (Bernardo et al., 2019). For example, German universities commonly provide large lecture courses with weekly lectures and a final exam at the end of the academic term. However, lecture attendance is often optional, leaving students responsible for adequately preparing for the final exam. Students’ responsibility for their own learning success might have been even higher under pandemic conditions, when only distance teaching was possible (Boström et al., 2021; Lockl et al., 2021). At the same time, many courses offer a variety of evidence-based learning activities going far beyond lecture attendance such as additional literature or practice testing (Bosch et al., 2021; Boser et al., 2017). Engaging in these activities has been empirically shown to enhance learning outcomes (e.g., Bosch et al., 2021; Dunlosky et al., 2013). As knowledge about educational psychology is highly relevant for preservice teachers’ own learning and their future occupation, it is crucial for them to seize learning opportunities (Moser et al., 2021).

However, many students engage in learning activities less frequently than they originally intended at the start of the academic term, resulting in an intention–behavior gap (Blasiman et al., 2017; Bosch et al., 2021). One aim of this study is to examine intention stability and the gap across a variety of learning activities in an educational psychology lecture course. Furthermore, individual characteristics like volitional competencies (VOCO; Spinath, 2005) might play a decisive role in explaining the size of the intention–behavior gap. VOCO consist of a set of skills which are necessary in self-regulated learning processes. The current study investigates if VOCO can predict the size of the intention–behavior gap and thus possibly represent starting points for interventions to close the gap.

The Intention–Behavior Gap in Higher Education

Intentions to show a certain action play a crucial role in explaining behavior (e.g., Ajzen, 2012; Madden et al., 1992). Consequently, established theories of action regulation, such as the theory of planned behavior, include intentions as a key precursor of behavior (e.g., Ajzen, 1991 , 2012; La Barbera & Ajzen, 2021). Within these theories, intention is defined as the willingness to engage in a particular behavior (Ajzen, 1991, 2012). More than 2,000 empirical studies have demonstrated positive correlations between intentions and behavior in various areas (La Barbera & Ajzen, 2021) such as physical activity (see Rhodes & Dickau, 2012), dieting (see Riebl et al., 2015), or environmental behavior (see Morren & Grinstein, 2016). Still, studies examining action taking in various contexts found that intentions often fail to translate into behavior, leading to an intention-behavior gap (e.g., Henderikx et al., 2017; Sniehotta et al., 2005; for an overview, see Sheeran, 2002). The intention–behavior gap can occur not only if the behavior is absent but also if its frequency or intensity deviates from initial intentions (see e.g., Bosch et al., 2021; Sniehotta et al., 2005).

In educational settings, relatively few studies have examined the intention–behavior relationship, mostly reporting moderate intention–behavior correlations. Sheeran et al. (1999) found that intentions explained 34% of variance in study behavior, operationalized by the number of days students studied for more than 3 hr. Other studies tried to explain variance in more specific learning activities such as lecture attendance (e.g., Hollett et al., 2020). They reported a correlation of r = .53 between intention to attend lectures and actual attendance. Still, Branca and Slusser (2022) observed an intention–behavior gap in this context. These findings align with a recent study in an educational psychology lecture course, which examined specific learning activities, including lecture attendance, submission of voluntary essays, and practice testing among others (Bosch et al., 2021). Intentions to engage in learning activities (short: intentions) and actual engagement in these activities (short: actual engagement) showed correlations of r = .34 and r = .50 across two samples. On average, students engaged in learning activities less than they had intended. Blasiman et al. (2017) also found intention–behavior gaps across 10 specific learning activities.

Development of Intentions at the Beginning of an Academic Term

Ajzen acknowledged the imperfect stability of intentions between measurement and behavior as a boundary of his theory (Madden et al., 1992). Empirical findings from health psychology support the idea that only temporally stable intentions serve as reliable predictors of subsequent behavior (Conner & Godin, 2007; Sheeran & Webb, 2016). In contrast, unstable intentions are more prone to change, for example with additional information (Jekauc et al., 2025). If intentions fluctuate over the observed period of time due to changing conditions, the gap might result from shifts in intentions leading to adjusted behaviors, which are different from initial intentions.

During the transition from secondary to higher education, students hold a variety of expectations which often cannot be fulfilled in the new context (Winstone & Bretton, 2013). As a result, they need to revise their mental models of how studying in higher education works during their first weeks and months in higher education (Boser et al., 2017; Winstone & Bretton, 2013). Even in later semesters, students must adjust their expectations regarding workload and task difficulty, which are often unclear at the start of a term. For example, many overestimate the available amount of time for a certain subject (Benden & Lauermann, 2022; Dresel & Grassinger, 2013). After a few weeks, though, the initial biases should diminish, leading to more realistic intentions (Benden & Lauermann, 2022). Similar to the short-term motivational development as well as changes in performance goal profiles in higher education courses, most intention adjustments should thus occur within the first half of the academic term (Benden & Lauermann, 2022; Lee et al., 2017; Sander et al., 2024).

Volitional Competencies

Engaging in learning activities requires both motivation and volition (Trautner et al., 2025; Wolters, 2003). In their Rubicon Model, Heckhausen and Gollwitzer (1987) distinguish four stages of the action process. The first stage, the predecisional phase, involves motivational processes that lead to intention formation. The second stage, the preactional phase, involves translating intentions into more specific, proximal goals and action plans, including considerations of when, how, and under which circumstances the behavior will be performed. The third stage, the actional phase, comprises the initiation and maintenance of goal-directed behavior. Finally, in the postactional phase, the behavior and its outcomes are evaluated.

During the preactional as well as the actional phase, competencies are necessary that help students translate their intentions into action. For these phases, Schaller and Spinath (2017) define VOCO. VOCO are conceptualized as comprising two groups of subcompetencies (Spinath, 2005). VOCO regarding goal setting and action planning (VOCO-GA) are more important in the preactional phase to formulate goals in such a way that achieving them becomes more likely and plan actions accordingly. The subcompetencies comprising VOCO-GA (Schaller & Spinath, 2017; Spinath, 2005) include the following: To facilitate goal-directed behavior, goals should be structured according to the SMART framework (Doran, 1981; Weintraub et al., 2021), which stands for specific, measurable, attainable, relevant and time bound. Additionally, goals should contain an appropriate level of challenge. Action planning involves identifying effective ways to show the intended behavior and anticipating possible obstacles. VOCO regarding resource activation (VOCO-RA) are important in the actional phase to actually achieve goals or implement actions. VOCO-RA include using positive self-instruction, generating an appropriate level of pressure, and rewarding oneself (Schaller & Spinath, 2017; Spinath, 2005). Students can employ techniques such as controlled breathing to enhance concentration, optimize their learning environment to suit their needs, and enrich the learning situation (e.g., by combining theory with practical tasks). Moreover, they can imagine their future to help them focus on their distal goals (e.g., the profession which they want to qualify for with their studies). If students have both high VOCO-GA and VOCO-RA they should be more successful in mastering the preactional and actional phase of the Rubicon Model (Heckhausen & Gollwitzer, 1987) by effectively implementing their intentions regarding their learning activities (Schaller & Spinath, 2017).

Previous research on strategies that can be classified as VOCO found that they are related to effort in academic contexts (Schwinger et al., 2009; Schwinger & Stiensmeier-Pelster, 2012). Furthermore, VOCO-GA have been shown to predict subsequent goal attainment and goal satisfaction (Schaller & Spinath, 2017). Similarly, Breitwieser and Brod (2022) found that volitional control is related to academic goal achievement (for a discussion of the inconsistently used terminology regarding motivational and volitional strategies, see Trautner et al., 2025).

Learning in Higher Education Under Pandemic Conditions

The shift to distance education in March 2020 due to the global pandemic posed new challenges for students’ learning. Reduced social and institutional integration, unexpected changes in teaching formats, and personal concerns have complicated studying under acute pandemic conditions (e.g., Besser et al., 2022; Fong, 2022; Lee et al., 2021). By pandemic conditions, we refer specifically to the restrictions on movement and gatherings during the acute emergency phase of the COVID-19 pandemic, particularly as they affected teaching and learning in higher education. Biwer et al. (2021) discovered that, under pandemic conditions, students were less motivated and had difficulties with self-regulated learning. However, some prior studies unexpectedly found higher motivation and lower motivational decrease under pandemic conditions (Sander et al., 2024) or no differences between regular and pandemic conditions (Bosch & Spinath, 2023; Pasion et al., 2021). Although motivational decrease in some studies was lower, it still existed (Sander et al., 2024). Furthermore, some studies report a higher use of learning activities under pandemic conditions compared to regular conditions (Biwer et al., 2021; Bosch & Spinath, 2023), while others found reduced academic engagement (Pasion et al., 2021). VOCO might have gained relevance since learning in higher education becomes even more self-regulated under pandemic conditions (Boström et al., 2021; Daumiller et al., 2023; Lockl et al., 2021). Though, prior research did not find structural differences in learning processes under pandemic conditions. In summary, findings about learning in higher education under pandemic conditions are inconsistent and do not point to specific differences between teaching conditions.

The Current Study

The main goals of the study are to examine the intention–behavior gap in the context of psychology teaching and learning more closely by using a longitudinal design and furthermore to identify possible predictors of the size of the gap by investigating VOCO.

Hypotheses

Specifically, we test the following hypotheses:

RQ1: How do intentions develop during an academic term, and how do intentions translate into actual engagement in learning activities?

H1: Intentions to engage in learning activities in the middle of the academic term (t2) are lower than initial intentions (t1) in both conditions.

H2: Intentions to engage in learning activities (t1 and t2) predict actual engagement in learning activities (t3) in both conditions.

H3: Actual engagement in learning activities (t3) is lower than intended (t1 and t2) in both conditions.

RQ2: Can VOCO predict the size of the intention–behavior gap?

H4: Higher VOCO are associated with more engagement in learning activities (t3) in both conditions.

H5: Higher VOCO predict a smaller intention–behavior gap (t3–t1) in both conditions.

Method

Design and Procedure

The longitudinal study was conducted in an introductory lecture course in educational psychology. Preservice teachers with various combinations of at least two subjects attended the course. Each academic term, the course consisted of around 15 weekly lecture sessions on Thursdays, with each 90-min session covering a central topic in educational psychology (e.g., motivation, intelligence). Under pandemic conditions, these lecture sessions were held online. Under both conditions, lectures mainly consisted of the instructor delivering the content in a largely monologic manner, occasionally posing questions to students and allowing opportunities for student questions. Lecture slides and additional literature were at disposal, and students were encouraged to form (remote) study groups. Over the course of the academic term, students had the opportunity to submit essays in 8 designated weeks. In these weeks, the lecture slides contained an essay question concerning the session's topic, which required knowledge from the lecture as well as critical thinking and creative ideas. Students who handed in their essays on time received individual feedback on their texts from student tutors. Furthermore, students could participate in online surveys on five occasions throughout the term. These surveys allowed students to self-test their knowledge on course topics with 15–55 true–false items. Results of the knowledge test items were presented after the completion of each online survey. Beyond the knowledge test, the online surveys contained questionnaires assessing psychological variables related to learning in the course. Thereby, the online surveys at the beginning (t1), in the middle (t2), and at the end of the academic term (t3) served as measurement points for the present study. Each survey took approximately 10–30 min to complete depending on the included scales. Participation in the online surveys was voluntary. To ensure anonymity, responses were matched across measurement points using individual self-generated codes. Participants were informed of the purpose and content of the questionnaires in writing and could refuse to participate without any disadvantages. No formal review was required to conduct an anonymous online survey such as the current study, in accordance with the ethical guidelines for research involving human subjects in Germany.

Samples

The overall sample consists of five cohorts of preservice teachers (N = 675), who participated in the lecture course “Introduction to Educational Psychology” at Heidelberg University, Germany between summer term 2019 and summer term 2021. Inclusion required participation in the study at least at t1 and at one additional measurement point, as well as consent to the use of the data for research purposes. However, due to major changes in study conditions during the global pandemic, we defined two subsamples: participants who took the course under regular conditions and those who took the course under pandemic conditions. For clarity and ease of interpretation, we describe the two subsamples separately. Students were included in the subsamples if they participated at t1 and either t2 or t3 over the course of the corresponding term.

Regular Conditions

Students in this subsample participated in the lecture course in summer term 2019 or winter term 2019/20. The final subsample consisted of N = 267 participants (48% of the 555 students originally enrolled in the course). Of the N = 267 students who participated in the study at t1, n = 206 (77.2%) students participated at t2 and n = 212 (79.4%) at t3. The mean age was M = 21.85 years (SD = 3.32) and the mean number of semesters at university was M = 4.50 (SD = 2.35). More than two-thirds of the sample identified as female (69.9%), while 29.3% identified as male and 0.8% as inter or diverse. Participants’ mean high-school grade point average was M = 1.95 (SD = 0.58).

Pandemic Conditions

Students in this subsample participated in the lecture course in summer term 2020, winter term 2020/21, or summer term 2021. The final subsample consisted of N = 408 students (45% of the 908 students originally enrolled in the course). Of the N = 408 students who participated in the study at t1, n = 369 (90.4%) students participated at t2 and n = 212 (65.2%) at t3. On average, they were M = 21.25 years old (SD = 2.65) and had been attending university for M = 4.13 (SD = 2.68) semesters. More than two-thirds of the sample identified as female (72.2%), while 27.3% identified as male and 0.5% as inter or diverse. Participants’ mean high-school grade point average in this sample was M = 2.05 (SD = 0.58).

Measures

Intentions and Engagement in Learning Activities

Intentions were assessed at t1 and t2 using items measuring the intentions to use six specific learning activities. The recommended learning activities included: attending lectures (a), reviewing lecture slides (b) and literature (c), submitting voluntary essays (d), participating in the self-testing online surveys (e), and discussing with a study group (f). Participants rated on a 5-point scale (1 = not at all to 5 = very much) how much they were planning to use the recommended learning activities.

The actual engagement in learning activities was assessed at t3, using the same items. Now participants rated how much they used the recommended learning activities.

Volitional Competencies

VOCO were assessed at t1 using the two VOCO subscales of the motivation-related competencies questionnaire (Schaller & Spinath, 2017). From these subscales (VOCO-GA: 15 items, VOCO-RA: 21 items), all items were included. Each item was rated on a 5-point scale (1 = does not apply to 5 = does apply). Example items for the scales are “I set goals in my studies so that I can achieve them” (VOCO-GA) and “For studying I choose an environment in which I can concentrate well” (VOCO-RA). Internal consistency was high for VOCO-GA (α = .84), while VOCO-RA showed an acceptable but slightly lower reliability (α = .78). Schaller and Spinath (2017) reported comparable internal consistencies.

Statistical Analyses

We used SPSS Statistics version 29.0 (IBM Corp, 2023) for all analyses. Analyses were conducted using all available data for each respective model (pairwise deletion). Thus, cases with missing values on specific variables were excluded only from the corresponding analyses. This approach ensured that the maximum number of valid responses was retained for each analysis. Inspection of missing data patterns revealed no systematic bias related to key demographic or study variables. We used mixed-design repeated-measures ANOVAs to compare intentions at t1 and t2 (H1) as well as intentions at t1 and t2 and behavior at t3 (H3). We conducted these analyses both for the overall mean across all learning activities and for each activity separately (with Bonferroni-correction). Moderated multiple regression analyses were used to analyze the relationship between intentions and behavior (H2). We calculated the partial correlation coefficients from the square root of uniquely explained variance by each predictor. We conducted further moderated multiple regression analyses to predict the use of learning activities (H4) and the size of the intention–behavior gap by VOCO (H5) controlling for teaching mode.

Results

Preliminary Analyses

A visual examination using boxplots revealed few univariate outliers. However, comparisons between means and 5% trimmed means (Osborne & Overbay, 2004) showed only small deviations (< 5%), indicating that the presence of outliers did not substantially affect central tendency estimates. We therefore did not exclude any outlier data from further analyses. To assess normality, skewness and kurtosis values were examined for all variables. Results indicated that most variables were approximately normally distributed, except for some of the specific learning activities, which showed positive kurtosis and moderate negative skewness. Variance inflation factors for all predictors were below 10 and tolerance values were above .1, indicating that multicollinearity was not a concern in the analyses. To examine potential systematic attrition, we compared participants who completed the third measurement point with those who dropped out on all available background variables. The only significant difference emerged for high school GPA, with participants who remained in the study showing slightly better grades than those who dropped out. No differences were found for any of the variables relevant to the present analyses. Table 1 shows descriptive data.

Table 1.

Means and Standard Deviations of Descriptive Variables.

	Sample	n_t1	Intention_t1 (SD_t1)	n_t2	Intention_t2 (SD_t2)	n_t3	Use_t3 (SD_t3)	n_t1t3	Difference Use – Intention_t1t3 (SD_t1t3)	n_t2t3	Difference Use – Intention_t2t3 (SD_t2t3)
Attending lectures	Regular	265	4.45 (0.94)	196	4.43 (1.07)	200	3.99 (1.34)	200	−0.49 (1.15)	143	−0.36 (0.87)
Attending lectures	Pandemic	405	4.64 (0.71)	367	4.50 (1.02)	259	4.39 (1.15)	258	−0.27 (1.03)	222	−0.20 (0.68)
Reviewing lecture slides	Regular	264	4.34 (0.74)	196	4.37 (0.91)	200	3.68 (1.31)	200	−0.65 (1.23)	143	−0.51 (1.05)
Reviewing lecture slides	Pandemic	405	4.49 (0.66)	366	4.52 (0.77)	258	4.21 (1.04)	257	−0.28 (1.01)	221	−0.30 (0.92)
Reviewing literature	Regular	264	2.58 (1.13)	196	2.13 (1.12)	198	1.39 (0.74)	197	−1.17(1.02)	142	−0.68 (0.92)
Reviewing literature	Pandemic	405	2.84 (1.09)	365	2.09 (1.06)	260	1.56 (0.97)	258	−1.26 (1.06)	220	−0.46 (0.95)
Submitting essays	Regular	264	3.73 (0.93)	196	3.38 (1.31)	200	2.26 (1.30)	199	−1.46 (1.40)	144	−0.95 (1.26)
Submitting essays	Pandemic	406	4.22 (0.72)	368	4.53 (0.70)	261	3.99 (1.08)	260	−0.22 (1.17)	224	−0.50 (0.92)
Participating in self-testing	Regular	265	4.63 (0.59)	197	4.84 (0.52)	199	4.50 (0.99)	199	−0.15 (1.08)	143	−0.08 (0.73)
Participating in self-testing	Pandemic	402	4.69 (0.52)	363	4.85 (0.41)	258	4.72 (0.64)	254	0.01 (0.73)	218	0.01 (0.47)
Discussing with study groups	Regular	264	3.26 (1.26)	196	3.10 (1.30)	198	2.56 (1.30)	197	−0.72 (1.32)	142	−0.47 (1.20)
Discussing with study groups	Pandemic	405	3.49 (1.14)	367	2.91 (1.34)	260	2.23 (1.33)	258	−1.20 (1.22)	223	−0.60 (0.97)
Overall	Regular	261	3.83 (0.51)	197	3.71 (0.61)	195	3.07 (0.61)	193	−0.77 (0.59)	139	−0.49 (0.52)
	Pandemic	393	3.51 (0.56)	368	3.90 (0.50)	251	3.51 (0.56)	241	−0.54 (0.50)	216	−0.35 (0.42)
	Sample	n	Mean (SD)
VOCO-GA	Regular	246	3.69 (0.49)
	Pandemic	391	3.75 (0.52)
VOCO-RA	Regular	246	3.45 (0.52)
	Pandemic	389	3.41 (0.50)

Note. VOCO-GA = volitional competencies of goal setting and action planning; VOCO-RA = volitional competencies of resource activation.

All activities were measured with a 5-point scale (1 = not at all to 5 = very much). The difference scores reflect the means of individual difference scores. We report item-wise sample sizes for activities and scale-wise sample sizes for VOCO.

The VOCO-scale ranged from 1 = does not apply at all to 5 = totally applies.

For all t-tests, we examined the equality of variances using Levene's test. Whenever Levene's test was significant, we reported the t-test results with adjusted degrees of freedom based on the Welch correction. Two-sample t-tests comparing participants under regular and pandemic conditions revealed significant differences in age (t(473.37) = 2.49, p = .013, Cohen's d = 0.21) and high-school GPA (t(667) = −2.17, p = .031, Cohen's d = −0.17). These results indicate that participants under regular conditions were slightly older and had better high-school GPAs. Participants under pandemic conditions showed significantly higher initial intentions (t(652) = −5.77, p < .001, Cohen's d = −0.46), intentions at t2 (t(340.13) = −3.81, p < .001, Cohen's d = −0.36) and engagement in learning activities (t(444) = −7.93, p < .001, Cohen's d = −0.76). Furthermore, these participants showed smaller intention−behavior gaps between t1 and t3 (t(375.68) = −4.30, p < .001, Cohen's d = −0.42) and between t2 and t3 (t(251.67) = −2.67, p = .008, Cohen's d = −0.30). Given these differences, we included teaching mode as a moderator in all following analyses.

RQ1: How Do Intentions Develop During an Academic Term, and How Do Intentions Translate into Engagement in Learning Activities?

To examine discrepancies in intentions between t1 and t2 controlling for teaching mode, we conducted mixed-design repeated-measures ANOVAs. Results indicated that overall intention levels were lower at t2, F(1, 550) = 61.55, p < .001, partial η² = .10. Bonferroni-corrected ANOVAs (α < .007) revealed significant negative discrepancies for attending lectures, reviewing literature, and discussing with study groups. Intentions did not differ significantly for reviewing lecture slides or submitting essays and there were higher intentions to participate in self-testing at t2. Furthermore, we found significant interactions between teaching mode and the intentions for submitting essays and discussing in study groups. Participants under pandemic conditions had even stronger intentions for submitting essays at t2 than at t1 while the intentions decreased under regular conditions. However, participants under pandemic conditions showed a greater negative discrepancy of intentions for discussing in study groups than participants under regular conditions (see Table 1). Overall, intentions at t1 and t2 were highly correlated, r = .61, p < .001. Table 2 shows detailed results of mixed-design repeated-measures ANOVAs for intentions between t1 and t2.

Table 2.

Intention Discrepancies Between t1 and t2.

		Df	F	p	Partial η²
Attending lectures	Time	1, 560	5.31	.022	.009
Attending lectures	Time × Teaching Mode	1, 560	1.86	.173	.003
Reviewing lecture slides	Time	1, 558	0.154	.695	.000
Reviewing lecture slides	Time × Teaching Mode	1, 558	0.154	.695	.000
Reviewing literature	Time	1, 556	200.41	<.001	.265
Reviewing literature	Time × Teaching Mode	1, 556	5.49	.019	.010
Submitting essays	Time	1, 561	1.11	.293	.002
Submitting essays	Time × Teaching Mode	1, 561	51.72	<.001	.084
Participating in self-testing	Time	1, 555	46.94	<.001	.078
Participating in self-testing	Time × Teaching Mode	1, 555	0.50	.481	.001
Discussing with study groups	Time	1, 558	57.05	< .001	.093
Discussing with study groups	Time × Teaching Mode	1, 558	12.54	<.001	.022
Overall intentions	Time	1, 550	61.55	< .001	.101
Overall intentions	Time × Teaching Mode	1, 550	.09	.760	.000

Note. All activities were measured with a 5-point scale (1 = not at all to 5 = very much).

We conducted moderated multiple regression analyses to examine the relationship between students’ intentions at t1 and t2 and their actual engagement in learning activities at t3 controlling for teaching mode. Overall intentions at both t1 (partial r = .22, p < .001) and t2 (partial r = .33, p < .001) were significantly associated with subsequent engagement. We found no significant interaction effects between intentions and teaching mode. Associations between intentions at t1 and t2 and engagement in learning activities at t3 are reported in Table 3.

Table 3.

Associations Between Intentions and the Corresponding Engagement in Learning Activities.

	Attending Lectures	Reviewing Lecture Slides	Reviewing Literature	Submitting Essays	Participating in Self-Testing	Discussing With Study Groups	Overall Intentions
Overall model R²	.62***	.31***	.36***	.51***	.06***	49***	.47***
Intention (t1)	.11*	.20***	.18***	.03	.00	.12*	.22***
Intention (t2)	.61***	.29***	.38***	.33***	.11*	.49***	.33***
Teaching mode	.10*	−.04	.06	.00	.08	.11*	.07
Teaching mode* intention (t1)	−.04	.00	−.03	.00	.06	−.00	−.03
Teaching mode* intention (t2)	−.08	.03	−.06	−.09	.03	−.07	−.06
N	365	363	359	367	359	362	347

Note. All reported effect sizes are partial correlations (partial r), derived from the respective model terms. R² indicates the variance explained by the overall model. Teaching mode is coded as follows: 1 = pandemic conditions, 0 = regular conditions. The reported sample sizes refer to the number of observations included in each model.

*p < .05,*** p < .001.

Replicating prior findings (Blasiman et al., 2017; Bosch et al., 2021), we identified an intention-behavior gap in students’ learning activities, except for participation in self-testing. The mixed-design repeated-measures ANOVA (α < .007, Bonferroni-corrected) demonstrated a significant overall intention–behavior gap between t1 intentions and t3 actual engagement, F(1, 432) = 636.27, p < .001, partial η² = .60. Additionally, the interaction with teaching mode was significant mirroring a smaller intention-behavior gap under pandemic conditions, F(1, 432) = 636.27, p < .001, partial η² = .04. The same pattern applied to the gap between t2 intentions and t3 actual engagement, F(1, 353) = 279.25, p < .001, partial η² = .44. Again, the interaction shows a smaller gap under pandemic conditions, F(1, 353) = 7.80, p = .006, partial η² = .02. Table 4 shows detailed results for intention–behavior gaps.

Table 4.

Intention–Behavior Gaps for Individual Learning Activities.

	N	Intention Measurement Point	Intention (SD)	Use_t3 (SD_t3)	Main Effect Partial η²	p	Interaction Effect Partial η²	p
Attending lectures	458	t1	4.58 (0.79)	4.21 (1.25)	.11	<.001	.01	.030
Attending lectures	365	t2	4.56 (0.99)	4.29 (1.20)	.12	<.001	.01	.049
Reviewing lecture slides	457	t1	4.42 (0.70)	3.97 (1.19)	.15	<.001	.03	<.001
Reviewing lecture slides	364	t2	4.52 (0.77)	4.14 (1.09)	.14	<.001	.01	.048
Reviewing literature	455	t1	2.70 (1.09)	1.48 (0.87)	.57	<.001	.00	.329
Reviewing literature	362	t2	2.04 (1.08)	1.49 (0.88)	.26	<.001	.01	.036
Submitting essays	459	t1	4.00 (0.83)	3.24 (1.46)	.30	<.001	.19	<.001
Submitting essays	368	t2	4.12 (1.13)	3.45 (1.41)	.31	<.001	.04	<.001
Participating in self-testing	453	t1	4.70 (0.52)	4.64 (0.80)	.01	.095	.01	.064
Participating in self-testing	361	t2	4.88 (0.37)	4.84 (0.54)	.01	.154	.00	.321
Discussing with study groups	455	t1	3.36 (1.20)	2.37 (1.33)	.36	<.001	.03	<.001
Discussing with study groups	365	t2	2.92 (1.34)	2.37 (1.34)	.19	<.001	.00	.277
Overall intentions	434	t1	3.96 (0.50)	3.31 (0.61)	.60	<.001	.04	<.001
Overall intentions	355	t2	3.83 (0.52)	3.43 (0.56)	.44	<.001	.02	.006

Note. We report item-wise sample sizes. The Bonferroni-corrected alpha level was α < .007. All activities were measured with a 5-point scale (1 = not at all to 5 = very much).

To provide an integrated test across all measurement points, we additionally conducted a 2 (teaching condition) × 3 (measurement point) mixed ANOVA. This analysis complements the analyses reported above by including all time points within a single model. The detailed results are reported in Supplemental Material S1.

RQ2: Can Volitional Competencies Predict the Size of the Intention–Behavior Gap?

We used moderated multiple regression analyses to predict students’ engagement in learning activities based on their VOCO and teaching mode as well as the interaction. VOCO-GA (partial r = .16, p < .001) and VOCO-RA (partial r = .10, p = .043) significantly predicted actual engagement. No significant interactions of VOCO and teaching mode occurred. In total, the model accounted for 22.0% of variance in the engagement in learning activities, F(5,426) = 23.70, p < .001. Detailed results from the regression analyses on engagement in individual learning activities are displayed in Supplemental Table S2.

To examine whether VOCO predict the size of the intention–behavior gap, we conducted moderated multiple regressions with VOCO-GA and VOCO-RA as predictors and teaching mode as moderator. We restricted the sample to participants whose intention–behavior gaps were negative, that is, they used learning activities less than initially intended, or zero. Neither VOCO-GA (partial r = −.00, p = .844) nor VOCO-RA (partial r = .00, p = .864) significantly predicted the size of the intention–behavior gap. No significant interactions of VOCO and teaching mode occurred. Though, the overall model reached statistical significance, R² = .05, F(5,378) = 4.24, p < .001. Models for specific learning activities revealed inconsistent findings, which are detailed in Supplemental Table S3.

Discussion

The intention–behavior gap has been observed in educational contexts before (e.g., Blasiman et al., 2017; Bosch et al., 2021; Henderikx et al., 2017; Sheeran et al., 1999). The present study extends prior findings by replicating the gap and investigating potential predictors in the context of teaching and learning of psychology. Specifically, we examined the stability of intentions over the first half of an academic term and the emergence of an intention–behavior gap in an educational psychology lecture course for preservice teachers. Furthermore, we explored whether VOCO could predict individual differences in the use of learning activities and the magnitude of this gap.

Longitudinal Development of Intentions and the Intention–Behavior Gap

Repeated measurements of intentions over the first half of the academic term revealed generally lower intentions at t2, mirroring declining patterns observed in other motivational constructs (see Benden & Lauermann, 2022; Lee et al., 2017; Sander et al., 2024). More broadly, our findings suggest that intentions, like motivation, are prone to changes in the first half of an academic term. Thereby, lower intentions in the middle of term might reflect decreasing motivation. According to the theory of planned behavior, attitudes toward a behavior—a key antecedent of intentions—are shaped by expectancies and values (e.g., Ajzen, 2012; Madden et al., 1992). Since studies which found the decline in motivation predominantly used an expectancy-value framework (Benden & Lauermann, 2022; Dresel & Grassinger, 2013; Sander et al., 2024), it is likely that similar mechanisms contribute to the declines in intentions and motivation. Furthermore, the observed discrepancies in intentions are relevant for research on the intention–behavior gap. Intentions measured in the middle of the academic term were more predictive of actual engagement than intentions measured at the beginning. These results support earlier considerations of students having unrealistic expectations at the beginning of an academic term (Benden & Lauermann, 2022; Sander et al., 2025). The extent of adaptation might depend on how realistic students’ initial intentions were (Benden & Lauermann, 2022; Sheeran & Webb, 2016). While efforts to establish realistic expectations at the start of the semester are important, adaptive adjustments by students throughout the semester, reflecting their ability to respond to evolving conditions, should also be considered a positive outcome. Thus, the need for adaptation might be reduced by better informing students at the beginning of the academic term about course topics, workload, and requirements (Sander et al., 2025). However, the adaptation should not be eliminated as it could be seen as an adaptive regulatory process. When assessing the gap, individual adaptation processes must be considered to draw valid conclusions about the magnitude of the gap (Jekauc et al., 2025).

Overall intentions accounted for 21.8% of explained variance in actual engagement, which is slightly more than in a previous study (Bosch et al., 2021). However, the larger proportion of variance was explained by intentions measured in the middle of the academic term, which were not examined in the previous study. There was no significant intention–behavior gap for participation in self-testing online surveys, possibly due to ceiling effects and reduced variance in this specific learning activity. Nevertheless, the gap emerged across all other learning activities, confirming prior findings (e.g., Blasiman et al., 2017; Bosch et al., 2021). The effect size was considerable for overall intentions and actual learning behavior, indicating that students, in general, engaged less than intended. The moderating effect of teaching mode was significant for certain individual learning activities such as reviewing slides, submitting essays, and discussing with study groups. These differences can partly be explained by the unique circumstances during the pandemic. For example, students might have hoped to be able to meet with study groups in person again at some point but ongoing restrictions might have hindered them.

VOCO as Predictors

As expected, we found a positive relation between VOCO-GA and VOCO-RA and engagement in learning activities. These findings align with existing research on volitional and self-regulatory competencies (Bäulke et al., 2018; Schwinger et al., 2009; Schwinger & Stiensmeier-Pelster, 2012; Sniehotta et al., 2005). The results emphasize the role of effective strategies for engaging in learning activities. Since the interaction with teaching mode was not significant, our results imply that VOCO may be equally important under pandemic and regular conditions. The findings provide another empirical basis for the need to develop and implement interventions aimed at enhancing self-regulatory skills (e.g., Bellhäuser et al., 2016; Bernacki et al., 2020; Lu & Wang, 2022).

However, higher VOCO did not predict a smaller intention–behavior gap. One possible explanation for this contradictory finding is that there was not enough variance in the magnitude of the gap to find effects. This might be due to the decision to only take those students into account whose behavior did not reach the intended level. Some students also managed to surpass their intentions. On the one hand, surpassing intentions might be seen positive as students engage even more in learning activities than intended and therefore are more likely to succeed. On the other hand, the gap might also occur due to an underestimation bias concerning students’ own capacities at the beginning of the academic term. If students underestimate their capacities at the beginning of the semester, it could lead to them setting their goals too low and engaging less than they could. This could be interpreted negatively as a sign of low accuracy in self-perception. The latter scenario underscores the need for interventions aimed at supporting students in realistically assessing their capabilities. Therefore, it is not clear whether this form of gap is to be judged positive or negative so that insights about its effects are required.

Limitations

One critical issue in this study is participant dropout over time, which poses potential bias concerns. For example, it is likely that participants who use learning activities much less than intended either dropped out of the study since participation in the online surveys itself constituted a learning activity. This dropout pattern could inflate estimates of learning behavior and thus reduce the observed associations. In addition, since participation was voluntary, the sample may be subject to self-selection bias. It is possible that more motivated or higher achieving students were more likely to participate from the beginning. To mitigate these issues in future studies, one approach would include strengthening the motivation to take part in the online surveys. Additionally, including data from digital learning platforms could provide a more comprehensive and objective measure, containing information not only about the frequency but also about the duration and exact processes of visiting the platform (Ye & Pennisi, 2022). However, not all learning activities occur in digital learning environments (e.g., study groups, reviewing literature), meaning digital trace data cannot fully replace self-reports. Moreover, ensuring data security and successfully linking electronic data with self-reports remain methodological challenges.

This study relied on self-report data, which is susceptible to biases such as social desirability and retrospective inaccuracies. Because participation in the online surveys was voluntary, effects of self-report biases should mostly be avoided. The longitudinal design further strengthens the data quality. However, future studies should assess data even more regularly to enhance self-report accuracy. Furthermore, since online surveys were measurement points and learning activity at the same time, the results concerning this specific learning activity must be interpreted cautiously. Intention and behavior scales only included subjective estimations of students’ intended and actual engagement. Future studies could implement absolute metrics such as time spent for a learning activity.

VOCO could not predict the magnitude of the intention–behavior gap, suggesting that additional variables influence the extent to which students translate intentions into actual engagement. One potential explanation is that students initially overestimate how enjoyable or useful they will find certain learning activities, leading to disengagement after initial attempts. Furthermore, students might know how helpful learning activities are but decide not to use them for various reasons (David et al., 2024; Foerst et al., 2017), for example, a lack of motivation to use the activities itself. This so-called production deficiency might be addressed in interventions to close the intention–behavior gap. Motivational and self-regulatory mechanisms might help explain more variance in the magnitude of the intention–behavior gap. Further research should incorporate further predictors (e.g., variables from the theory of planned behavior) as well as outcomes (e.g., achievement, well-being and study satisfaction) of the intention–behavior gap. Furthermore, acknowledging the variability of size and possible causes of the intention–behavior gaps between students, person-centered approaches could contribute to a better understanding of the phenomenon and the underlying mechanisms.

Implications

The results of this study have several important implications for educational practice. First and foremost, VOCO play a crucial role for the engagement in learning activities and should be targeted not only in higher education but ideally also earlier in students’ educational careers.

Targeting the adjustment phase at the beginning of the academic term, information management might be a key area of improvement. That is, clearly informing students at the beginning of the academic term or even before about what to expect from the following courses. If information about attendance requirements, recommended learning activities, and requirements for exams is available directly at the beginning of the academic term, students can set their intentions more realistically, reducing the need for major adjustments in the first weeks of the academic term. Students might therefore feel more comfortable and less overwhelmed, which can positively influence their mental health and satisfaction with their learning (Benden & Lauermann, 2022; Winstone & Bretton, 2013).

Declines in intentions and the intention–behavior gap may reflect decreasing motivation. Therefore, instructors should aim to sustain students’ motivation throughout the semester. A smaller decline in motivation has been associated with positive outcomes, including higher learning achievement, greater satisfaction, and reduced dropout rates (Benden & Lauermann, 2022).

As intention–behavior gaps did not only vary between participants but also across different learning activities, instructors should supply a variety of learning activities that support sustained engagement throughout the academic term. However, it is essential to align the supply with evidence on the effectivity of learning activities (see Dunlosky et al., 2013) to maximize their impact on academic success (Bosch et al., 2021). By integrating effective and engaging learning activities, students can not only sustain their engagement but also achieve meaningful learning outcomes.

Conclusion

The present study examined the intention–behavior gap in higher education using a longitudinal design with two samples of preservice teachers under regular and pandemic conditions. The results indicate that in both samples intentions to engage in learning activities were lower in the middle of the term, suggesting that initial intentions are often adjusted based on experience and external constraints. A substantial intention–behavior gap could be identified across various learning activities, confirming previous findings. Higher VOCO explained an increased use of learning activities. These findings underscore the importance of strengthening VOCO as part of educational interventions. Furthermore, enhanced information management at the beginning of each academic term should reduce the need for early-semester adaptation processes. By addressing these factors, instructors can improve the quality of teaching and learning, support students’ self-regulatory processes, and ultimately foster greater academic success in educational psychology as one of the key areas in teacher education.

Supplemental Material

sj-docx-1-plj-10.1177_14757257261441073 - Supplemental material for Closing The Gap? Examining the Longitudinal Development of Intentions and the Intention–Behavior Gap in an Educational Psychology Lecture Course

Supplemental material, sj-docx-1-plj-10.1177_14757257261441073 for Closing The Gap? Examining the Longitudinal Development of Intentions and the Intention–Behavior Gap in an Educational Psychology Lecture Course by Vivien Rieder and Eva Bosch in Psychology Learning & Teaching

Footnotes

ORCID iD

Vivien Rieder

Ethical approval and informed consent statements

No formal review was required to conduct an anonymous online survey such as the current study, in accordance with the ethical guidelines for research involving human subjects in Germany. Participants were informed of the purpose and content of the questionnaires in writing and could refuse to participate without any disadvantages.

Author Contributions

Vivien Rieder: conceptualization, methodology, formal analysis, data curation, writing—original draft, writing—review and editing, and visualization; Eva Bosch: conceptualization, methodology, writing—review and editing, and supervision.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Conflicting Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Vivien Rieder is a research assistant and PhD student at the Department of Educational Psychology and the Institute of Education Science at Heidelberg University, Germany. Her research and teaching interests include motivation as a prerequisite for success in (self-regulated) learning, learning-related mindsets, diagnostic competencies of teachers, and talent development in education.

Eva Bosch completed her doctorate in the Department of Educational Psychology at Heidelberg University, Germany. Her research and teaching interests include evidence-based teaching and learning at the university level as well as students’ motivation as a prerequisite to engage in self-regulated learning.

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