Louise David

Especially in higher education, learners need to plan, monitor, and execute (i.e., self-regulate) most of their learning autonomously. Doing so effectively, for instance, by using effective learning strategies, is essential for academic success. Although students are becoming increasingly aware of the importance of using effective learning strategies, many continue to struggle to apply them and instead rely on less effective ones. This discrepancy between what is known to be effective and what students actually do represents a persistent challenge in promoting the use of effective learning strategies. Research has identified several potential barriers that may explain why students struggle to use effective learning strategies regularly. However, little is known about how these barriers interact in authentic learning contexts or how students can be supported to overcome them to incorporate effective learning strategies more sustainably. The present dissertation addresses this gap by investigating why students fail to use effective learning strategies sustainably and exploring the promotion of sustainable strategy use through behavioral change approaches.

To address the research aims, I conducted four studies. In chapter 2, I investigated how learners’ perceived mental effort and perceived learning relate to actual learning. In chapter 3, I explored university students’ study habits and how they form new and break existing habits within this context. In chapters 4 and 5, I investigate to what extent goal-setting via implementation intentions influences university students’ self-regulated learning and their use of distributed practice.

According to the cue-utilization theory (Koriat, 1997), learners are unable to directly access information about their own learning, and thus often use cues such as perceived mental effort to judge their learning. Past research indicates that learners often interpret their mental effort experience as a sign of low learning. However, so far, it is unclear how this commonly relates to actual learning outcomes. Using a meta-analytic structural equation model, I synthesized evidence on the relationships between perceived mental effort, monitoring judgments, and actual learning outcomes. I extracted data from 35 manuscripts with 83 independent samples, 236 effect sizes, and a total sample size of N = 3973. Across studies, perceived mental effort was negatively associated with monitoring judgments (β = -.19), meaning that higher perceived mental effort was linked to lower feelings of learning or understanding. Monitoring judgments were positively associated with actual learning (β = .29), meaning that higher feelings of learning or understanding, related to actual higher learning. Together, these effects produced an indirect negative relationship between perceived mental effort and actual learning outcomes via monitoring judgments (β = -.05). This pattern suggests that learners often interpret effortful learning experiences as signs of poor learning, which aligned with actual lower performance. In the contexts captured by the included studies, this interpretation was consistent with the cue-utilization theory. The latter suggests that perceived mental effort is a diagnostic cue used during monitoring of learning as it correlates with lower perceived learning and lower actual learning. The findings of the study highlight the need to support learners in interpreting their perceived effort during learning. While in the contexts of the included studies, higher perceived mental effort was a diagnostic cue correlating with actual lower learning, this might not always be the case. For example, in the context of desirable difficulties, interpreting perceived mental effort as a sign of poor learning might mistakenly cause students to disengage prematurely, since effort is an essential aspect of the effectiveness of desirable difficulties.

To gain insight into how students sustain the use of effective learning strategies, in chapter 3, I explored university students’ study habits and their experiences forming new and breaking old habits as habitual behavior is commonly a predictor of behavior. Using a qualitative approach, I invited 29 first-year students to six focus group discussions to gain richer understanding of their habitual study behaviors and challenges associated with incorporating effective learning strategies. Thematic analysis revealed that students’ learning strategy choices were often shaped by the type of assessment and the perceived efficiency of reaching short-term goals. While students demonstrated some metacognitive knowledge, behavioral change was described as effortful and undertaken only when deemed necessary, for example, if previous learning strategies did not help to pass an exam. Other students mentioned that they had intentions to change their study behavior, but were not sure how. Motivation fluctuated throughout the semester, influencing whether students acted upon their intentions to change. Furthermore, navigating life challenges such as the transition from high school to university affected students’ study behavior. The findings provided insights into the factors that shape students’ intentions to change their learning behaviors and highlight the need to align assessment methods with lifelong learning objectives. Moreover, supporting students in setting long-term academic goals that encourage the development of study habits could foster durable learning and consistent use of effective learning strategies to reach these long-term goals.

As goal-setting has been shown to be effective to initiate behavioral change in other domains, in chapter 4, I investigated whether goal-setting via implementation intentions could increase university students’ use of distributed practice. Additionally, I explored students’ SRL throughout a four-week university course. Using the experience sampling method (ESM), I monitored students’ (N = 85) daily use of distributed practice and other study behavior such as their study motivation, study effort and study satisfaction and compared how this differed between students who set specific implementation intentions to incorporate distributed practice and obstacle plans that could hinder them from achieving their goal (n = 35) compared to students who were merely informed about the benefits of distributed practice and goal-setting at the beginning of the study (n = 50). Although implementation intentions did not increase the use of distributed practice, students in this group adopted more effective strategies, such as practice testing early in the course, studying for shorter durations, reported lower study effort, and showed increased concentration toward the end of the course while performing similarly to the control group at the end-of-course exam. Across both groups, study behaviors shifted over time, with study duration, use of effective learning strategies, motivation, effort, and study satisfaction increasing near exams. Qualitative analysis of the implementation intentions suggests that students were generally able to formulate goals on how to regularly review previously learned content, yet these goals often lacked specificity and concreteness. Overall, the findings indicate that while implementation intentions did not directly increase the use of distributed practice, they may support more efficient study behavior, such as the earlier adoption of retrieval-based strategies. Furthermore, the results show that students’ learning behaviors change, with exam proximity strongly shaped how students allocate effort and motivation. These patterns underscored the importance of designing assessment methods that encourage consistent learning for long-term retention rather than short-term performance. Moreover, the study illustrates the value of examining self-regulated learning processes over extended periods using ESM to capture how students dynamically adjust their study behaviors in response to changing academic demands.

In chapter 5, I used a similar design as in chapter 4, and extended it by including three groups and adding a one-week follow-up ESM period five weeks later to investigate potential transfer effects. In the implementation intention group (N = 29), students formed implementation intentions, in the information-only group (N = 36), students received only information about distributed practice and its benefits, and the control group (N = 35) received no additional input. Over a four-week course, I used ESM (k = 1998) to capture daily study behavior such as learning strategy use, study duration, motivation, and effort. Five weeks later, we had a one-week follow-up ESM period (k = 343) to assess potential transfer of study behavior and goal achievement to a new course. Similar to chapter 4, results suggest that formulating implementation intentions did not significantly increase students’ use of distributed practice compared to control conditions. Instead, students’ individual intentions to use distributed practice predicted their actual engagement in distributed practice. Changes in study behaviors over time were primarily influenced by contextual factors, particularly exam proximity. Across groups, as exams approached, students reported greater effort, study time, concentration, extrinsic motivation, and satisfaction, along with reduced distraction. Independent of group, use of distributed practice in the next course was predicted by use of distributed practice during the main ESM period. These results suggest that shifts in students’ learning behaviors are driven more by exam proximity than by goal-setting and underscore the difficulty of translating intentions into consistent behavioral change. Especially for complex strategies like distributed practice that require sustained effort over time, educational interventions may be more effective when they help students establish an instigation habit that fosters initiation of regular studying. Moreover, assessment practices that reward long-term learning rather than short-term performance could better support students in developing durable self-regulated learning habits.

In chapter 6, I synthesized and discussed the results and implications of the studies in relation to the research questions of this dissertation and related scientific literature. The findings of this dissertation indicated that students’ engagement with effective strategies fluctuated over time, shaped by motivation, interpretation of mental effort during learning, and contextual pressures. Despite understanding which strategies are effective, many students failed to use them consistently as effort felt costly, benefits of using effective learning strategies were not salient to all students, and short-term performance could be achieved when using less effortful strategies. Effort was frequently misinterpreted as a sign of poor learning, creating a barrier to adopting desirable difficulties that require effort. The development of habits around effective study behaviors proved complex, potentially because learning strategies such as distributed practice involved multiple steps that were difficult to automatize. Sustained strategy use, therefore, likely depends not only on metacognitive knowledge but also on specific intentions to use them and environmental scaffolds and support that, for example, help interpret effort during learning, reduce effort required to engage in effective learning strategies, and align the utility of effective strategies with students’ goals. Ultimately, fostering consistent use of effective learning strategies likely requires educational environments that normalize effortful learning, provide support, and align incentives with long-term understanding rather than short-term performance.