Abstract: We investigated immediate learning benefits of retrieval tasks when followed by a self-regulated relearning opportunity, compared to a restudy task. We aimed to unravel the underlying metacognitive and motivational mechanisms. In a mixed-factorial design (N = 104), we manipulated review task (retrieval vs. restudy) as a within-subjects factor in two experimental sessions, and task granularity (coarse- vs. fine-grained) as a between-subjects factor. The retrieval task led to an immediate learning benefit compared to a restudy task in Session 1, but not across sessions. The review-task order influenced the emergence of an immediate retrieval-practice effect. Mediation analyses for Session 1 revealed that decreased levels of both judgments of learning (JOL) and self-efficacy partially mediated the retrieval-task effect on the learning outcome. Fine-grained tasks increased JOL regulation accuracy; however, this increase did not translate into better relearning. Retrieval tasks can improve learning outcomes, also after short delays, specifically when relearning opportunities are provided.

Unmittelbare Lerneffekte von Abrufaufgaben. Zur Rolle von selbst-reguliertem, wiederholtem Lernen, Metakognition und Motivation

Zusammenfassung: In der vorliegenden Studie untersuchten wir den Einfluss von Abrufaufgaben auf selbstgesteuertes, wiederholtes Lernen. Ziel war es, die zugrundeliegenden metakognitiven und motivationalen Mechanismen aufzuklären. Wir verwendeten ein gemischt-faktorielles Design mit den Faktoren Lernaufgabe (Abrufaufgabe vs. wiederholte Lernaufgabe) als Innersubjektfaktor in zwei Studiensitzungen sowie Granularität der Lernaufgabe (spezifische Aufgabenblöcke vs. globaler Aufgabenblock) als Zwischensubjektfaktor. Basierend auf einer Stichprobe von 104 Versuchspersonen zeigt die Studie, dass Abrufaufgaben zu einem unmittelbaren Lernvorteil in der ersten Sitzung führten. Es konnte allerdings kein Lerngewinn durch die Bearbeitung der Abrufaufgabe über beide Studiensitzungen hinweg nachgewiesen werden. Mediationsanalysen ergaben, dass sowohl die metakognitive Überwachung als auch die Selbstwirksamkeiterwartung auf motivationaler Ebene den abruf-basierten umittelbaren Lerngewinn in der ersten Sitzung partiell mediierten. Als praktische Implikation könnten Lehrende dazu motiviert werden Abrufaufgaben auch zur Verbesserung des unmittelbaren Lernerfolgs einzusetzen, v. a. wenn die Gelegenheit zum wiederholten Lernen gegeben wird.

Students increasingly organize their learning in largely unsupervised learning environments outside the traditional classroom, such as massive open online courses (MOOCs; McCarthy et al., 2022) or other forms of self-regulated learning. To exploit such learning opportunities, students need to effectively monitor and regulate their learning on their own. However, they often appear unable to do so (for an overview, see Bjork et al., 2013). Against this background, there is growing interest in investigating self-regulated relearning in terms of making study decisions and allocating relearning time (de Bruin et al., 2020). One way to enhance monitoring and regulation quality in self-regulated relearning is to provide students with retrieval opportunities during learning (e.g., Soderstrom & Bjork, 2014). Experiencing successful and unsuccessful retrieval attempts enables students to improve their monitoring and to allocate the time to relearn information more efficiently. In this study, we had two main goals. First, we investigated whether and how retrieval tasks influence students’ relearning when complex materials (i.e., expository texts) are used, extending prior findings of test-enhanced relearning of word lists (e.g., Soderstrom & Bjork, 2014). Second, we aimed to unravel the metacognitive and motivational mechanisms that potentially underly this immediate, retrieval-based learning gain.

Benefits of Retrieval Tasks

Retrieval practice of previously learned information (e. g., by retrieval tasks) is a highly effective learning technique (Kubik et al., 2021; Roelle, Endres et al., 2022; Rowland, 2014; Yang et al., 2021). Practicing retrieval can slow down the forgetting of newly acquired knowledge as compared to restudy. This so-called direct retrieval-practice effect is robust and of medium effect size (e. g., Rowland et al., 2014; Yang et al., 2021). Retrieval tasks can also potentiate learning from subsequent relearning, which is referred to as indirect retrieval-practice effect (Arnold & McDermott, 2013; Endres & Renkl, 2022; Kubik et al., 2016; Roelle, Endres et al., 2022; Tempel & Kubik, 2017; or seminal work, see Izawa, 1966, 1970). There is a growing body of literature on the metacognitive and motivational benefits of retrieval tasks for optimizing the relearning of previously learned information, particularly the choice of what to relearn and for how long (e. g., Little & McDaniel, 2015; Soderstrom & Bjork, 2014; for an overview, see Bjork et al., 2013).

This study investigated the benefits of retrieval tasks on relearning of text materials and the corresponding effects on immediate learning outcomes. In addition to previous research (Little & McDaniel, 2015; Soderstrom & Bjork, 2014), we aimed to identify underlying metacognitive and motivational mechanisms, specifically when using more complex materials. Retrieval practice can motivate students to relearn information, and can also help them to diagnose what they know and what they do not know. Prior research suggests that metacognitive mechanisms (e. g., Rivers, 2021) and motivational mechanisms (e. g., Endres & Renkl, 2022) might contribute to these immediate benefits of retrieval tasks. Hence, we examined both metacognitive and motivational mechanisms simultaneously to understand their role in the effect of retrieval tasks on relearning and the immediate learning outcomes.

Retrieval Tasks Influence Self-Regulated Relearning

A retrieval task can influence relearning in many respects, such as the learning strategy the students apply, their motivation, and the amount of cognitive effort they invest. One important aspect is the time that students choose to spend on reviewing learning materials or specific sections of it. When learners use retrieval practice in a self-regulated learning session, they usually first engage in an initial study phase in which they learn the original material. Now learners have at least two ways of how to engage with the material in a review phase. They can either engage in retrieval practice or restudy the materials again. The two different review tasks provide the learner with different cues on how to regulate their relearning time on different parts of the materials after those tasks that might lead to differences in learning outcomes. In Soderstrom and Bjork (2014), for example, learners studied a list of pair associates (e.g., kitten–cat). The authors showed that retrieval as compared to restudy led to longer relearning times and better learning outcomes. However, this indirect testing effect was only found when the different review tasks were assigned to different learners (between-subjects manipulation of review task, Exp. 1). When the same learners performed both restudy and retrieval tasks, there were no benefits of the retrieval task due to potential order effects (within-subjects manipulation of review task, Exps. 3 and 4). More specifically, learning strategies related to the retrieval task (e.g., covert retrieval attempts and metacognitive strategies) were likely also applied to restudied items, and retrieval tasks might have enhanced the motivation to study the new material. There is more evidence for retrieval-based effects on self-regulated relearning with simple learning materials (e.g., Soderstrom & Bjork, 2014; Yang et al., 2018). Research on complex learning material (including multiple idea units), however, is rare. Beside the differences in initial learning from complex learning materials (Endres et al., 2023), monitoring and regulating learning are more difficult as well. For example, Little and McDaniel (2015) used expository texts and solicited judgments of learning (JOLs) before or after self-regulated relearning. Retrieval practice led to more accurate JOLs, stronger (negative) associations between JOLs and relearning times, and, most importantly, also improved learning outcomes on a delayed posttest.

Despite the importance of indirect effects of retrieval tasks and self-regulation, we found only a few studies investigating retrieval-practice effects on relearning complex material (e.g., Little & McDaniel, 2015). In addition, prior research has not yet tested whether the benefits of retrieval tasks are mediated by differences in relearning time. Furthermore, no specific learning process measures have been assessed to uncover potential metacognitive and motivational mechanisms. Finally, it is unclear whether motivational or learning -strategy effects carry over from previous review sessions when longer intervals (e.g., 1 week) between learning sessions are implemented, as may occur in authentic learning settings. In this study, we focused on the benefit of retrieval tasks for relearning of complex material and the resulting effects on learning outcomes.

Retrieval Tasks Enhance Metacognitionand Motivation

Metacognition and motivation do not influence learning independently, instead both are essential for each other (Efklides, 2011). Students need to be both metacognitively able to utilize retrieval experience for subsequent relearning opportunities and motivated to use the gained metacognitive information to effectively self-regulate their learning. Students with good monitoring performance might only know which sections of content to relearn; however, they may lack motivation to engage in effective relearning. Likewise, highly motivated students not knowing which topics to prioritize in relearning are unlikely to benefit much from their motivation. We therefore examined the effects of retrieval tasks on both metacognitive and motivational mechanisms and investigated their role for the potential learning benefits of retrieval tasks.

Metacognition

One instructional goal of retrieval practice is to enhance metacognition (Carpenter et al., 2020; Rivers, 2021), which involves two critical aspects: monitoring knowledge during learning, and regulating learning behavior to address knowledge gaps (de Bruin et al., 2020; Nelson & Narens, 1990). Retrieval tasks can actually help students monitor their knowledge and identify knowledge gaps (Amlund et al., 1986; Finn & Metcalfe, 2008; Hausman et al., 2021). Monitoring guides students as they restudy material (Little & McDaniel, 2015; Soderstrom & Bjork, 2014; Thiede et al., 2003). In particular, the amount of effort and time students devote to reviewing material depends on how they perceive the knowledge they have or have not yet acquired (Kornell & Finn, 2016; Nelson & Narens, 1990; Nückles et al., 2009).

Students often have difficulties to accurately assess how much they have learned (e. g., de Bruin & van Merriënboer, 2017). Unsuccessful attempts during retrieval practice reveal knowledge gaps and provide feedback on the accessibility of information in memory without requiring conscious monitoring or additional metacognitive strategies (Endres & Renkl, 2022). When students retrieve information, they also note how easy or difficult it is for them to remember certain information (Karpicke, 2009; Koriat & Bjork, 2006). These cues influence how they assess their current level of learning. Hence, retrieval tasks can result in more accurate predictions of how well students have learned a topic, and how well they will perform on future retrieval tasks (Kubik, Jemstedt et al., 2022; Roediger & Karpicke, 2006). As students usually overestimate their learning (e. g., Dunlosky & Rawson, 2012), more accurate cues should lead overall to lower JOL magnitudes. By measuring JOLs on different parts of the learning material, researchers can investigate how well students monitor their own learning and differentiate between different parts of the learning material in this regard.

Our study investigated whether the aforementioned improvement in monitoring through retrieval tasks can help students to structure their relearning more selectively. Retrieval tasks may influence not only how much time students spend on relearning overall, but also how they allocate time to different subtopics. After retrieval practice, students are expected to spend more time on specific knowledge gaps and aspects that they have not yet fully understood (Little & McDaniel, 2015; Soderstrom & Bjork, 2014; Thiede et al., 2003). This focused relearning of less memorable or poorly understood content should lead to better learning outcomes. We investigated whether retrieval tasks can actually influence how learners allocate relearning time across subtopics of an expository text. Retrieval tasks should help students invest more time in relearning the content areas in which they are most deficient.

Motivation

As mentioned before, the regulation of relearning depends both on students’ metacognition and on their motivation to engage in subsequent relearning (Efklides, 2011). One way to increase motivation is through regular retrieval practice, which has been found to motivate more regular study behavior in university courses (Roediger et al., 2011). Rather than trying to learn the material all at once (often just before an exam), as many students do, continuous retrieval practice can lead to more continuous self-regulated learning behavior (Blasiman et al., 2017). Even when practice tests are not graded, students are more motivated to learn continuously (Leeming, 2002; Lyle & Crawford, 2011). Notably, these motivational benefits of low-stake formative testing are particularly important given the negative connotations of high-stake exams (Clark & Svinicki, 2014). In addition, even a single retrieval attempt can increase motivation to further engage in subsequent relearning after retrieval (Abel & Bäuml, 2020).

One explanation of how low-stake tests impact motivation is provided by the knowledge deprivation hypothesis. This hypothesis states that students’ curiosity and motivation to engage with the content increase when they perceive knowledge gaps (Rotgans & Schmidt, 2011, 2014; Schmidt & Rotgans, 2021). Retrieval tasks are known to raise the likelihood of identifying knowledge gaps (Karpicke, 2009; Kubik, Jemstedt et al., 2022; Roelle et al., 2017), and students are motivated to reduce the discrepancy between what they know now and what they need to know (Dunlosky & Thiede, 1998; Thiede & Dunlosky, 1999).

However, motivation is a complex, multidimensional construct. A multitude of various theories aim to account for motivational factors, and have suggested different indicators of motivation that are relevant for learning (Urhahne & Wijnia, 2023). To cover important aspects of this complex construct, we measured several indicators of motivation. One indicator was self-efficacy, which refers to learners’ belief in their ability to perform a specific task or to achieve a particular goal. Self-efficacy is a fundamental construct in social cognitive theory (Bandura, 1986). Self-efficacy is shaped by various factors including recent experiences, which may include how the material was recently reviewed. Given that retrieval tasks provide students with more information about their current ability to recall the material, self-efficacy appears to be a relevant construct to consider. Another indicator often considered in the context of retrieval tasks is the mental effort that students invest in a learning task. Mental effort is used as an umbrella term that includes a wide range of cognitive activities that students choose to invest in a given task. As this active component of mental effort has a motivational component, it is of particular interest in this context (Klepsch & Seufert, 2021). Furthermore, the cognitive component of mental effort is thought to be a central mechanism in the direct effect of retrieval tasks on learning (Carpenter, 2009; Endres & Renkl, 2015), making mental effort an important factor to consider in this study. Finally, we considered situational interest, which refers to the temporary, context-specific component of interest (Hidi & Renninger, 2006). Situational interest arises from the feeling and the value that emerge in the immediate learning environment and can develop into a stable individual interest. We test the prediction that retrieval tasks will increase students’ motivation in terms of self-efficacy, mental effort, and situational interest, and thereby foster learning outcomes.

Benefits of Retrieval Tasks

On the Moderating Role of Task Granularity

Recent research suggests that the effectiveness of retrieval tasks is influenced by their granularity. This granularity can vary from fine-grained tasks that focus on specific facts or concepts to coarse-grained tasks, such as a free-recall tasks that cover entire texts (Eitel et al., 2022; Endres, Kranzdorf, et al., 2020; Wissman & Rawson, 2015). Recent studies have shown that finer-grained, specific retrieval tasks (i. e., asking multiple questions about the learned content) improve the retention of key concepts in the learning material (Eitel et al., 2022; Endres, Kranzdorf, et al., 2020). Conversely, coarse-grained retrieval tasks (i. e., asking fewer or just one question about the learned content) are better suited for obtaining a broader understanding (Endres, Kranzdorf, et al., 2020). In addition, there is preliminary evidence that coarse-grained retrieval tasks lead to higher levels of self-efficacy and situational interest than more fine-grained retrieval tasks. Fine-grained retrieval tasks provide students with a more accurate assessment of their current knowledge (Endres, Kranzdorf, et al., 2020). Given the limited amount of evidence, we explore the effects of retrieval-task granularity on relearning and the immediate learning outcomes.

The Present Study

We addressed two main research questions. First, we tested the hypothesis that retrieval tasks improve the immediate learning outcomes more than restudy tasks (i. e., learning-outcome hypothesis). Regarding the learning outcomes, we also explored potential effects of granularity and potential order effects of the study session.

Second, we investigated the potential mechanisms behind this immediate learning benefit. We measured relearning time as well as metacognitive and motivational learning process measures. We considered self-regulated relearning time primarily as a behavioral index that likely reflects both metacognitive regulation aspects (e. g., Little & McDaniel, 2015) and motivational aspects (Touré-Tillery & Fishbach, 2014). Similarly, self-efficacy, as the belief that one can successfully complete a task, reflects both metacognitive aspects and motivational aspects (Boekaerts, 2010; Zimmerman & Shanks, 2001).

We predicted that, as compared to restudy, retrieval tasks increase overall relearning time (i. e., the regulation hypothesis) and that this increased relearning time produce an immediate learning benefit evident in an indirect mediation path (i. e., the regulation–mediator hypothesis).

With respect to metacognition, we predicted that retrieval tasks, as compared to restudy, decrease the magnitude of JOLs (i. e., the monitoring hypothesis) and, as a result, students invest more overall relearning time, thereby improving their immediate learning outcomes (i. e., the monitoring–regulation–mediator hypothesis). Furthermore, we predicted a negative correlation between JOL magnitudes and relearning time across the text tasks. More importantly, we predicted that retrieval tasks increase the JOL–regulation–correspondence compared to restudy (i. e., the relative JOL–regulation–correspondence hypothesis). We also predicted a negative correlation between retrieval-task performance and relearning time across text subsections in the retrieval-task condition.

With respect to motivation, we predicted that retrieval, as compared to restudy, tasks increase situational interest and mental effort (i. e., the motivation hypothesis) and that these motivational increases lead to immediate learning benefits evident in an indirect mediation path (i. e., the motivation–mediator hypothesis).

Method

Design and Sample

We implemented a mixed 2 × 2 factorial design with review task (retrieval vs. restudy) as a within-subjects factor and task granularity (coarse-grained vs. fine-grained) as a between-subjects factor. To examine the benefits of the retrieval task without potential transfer effects (see Soderstrom & Bjork, 2014), we planned to also analyze the Session 1 data at a between-subjects level. Our dependent variables were learning outcomes (i. e., performance on an immediate posttest), mental effort, metacognitive monitoring, situational interest, self-efficacy, and relearning time (i. e., number of seconds participants choose to restudy the whole text material or the subsections of the text, respectively).

Sample size was calculated a priori by using G*Power (Version 3.1.9.2; Faul et al., 2007). We calculated the sample size for a mixed 2 × 2 factorial ANOVA. Using α = 0.05, a power of 1–β = 0.80, and an effect size of at least d = 0.57 (see meta-analysis of Yang et al., 2021, for retrieval-enhanced learning including a relearning opportunity), we determined a sufficient sample size of N = 28 for the interaction effect. However, prior research revealed that a retrieval task, followed by a self-regulated relearning opportunity, seemed only to produce an immediate memory benefit in a between-subjects design (Soderstrom & Bjork, 2014). Given this preliminary evidence and to exclude transfer effects between sessions, we aimed to also run a between-subjects ANOVA on data of Session 1 only at a between-subjects level. Therefore, we also calculated the sample size required for a between-subjects 2 × 2 factorial ANOVA. Again, with α = 0.05, a power of 1–β = 0.80, and an effect size of at least d = 0.57, we estimated N = 99 as a sufficient sample size.

Similar to this number of participants, 114 students participated in this study for course credit. Ten participants had to be excluded due to self-reported use of external help (e. g., taking notes, n = 5), responses that indicated non-serious study participation (n = 2), having already participated in a study using the same material (n = 4), or incomplete data (n = 1). We thus included 104 participants (age_{coarse granularity}: M = 25.69, SD = 10.71; age_{fine granularity}: M = 27.59; SD = 12.65). Participants were undergraduate students from a German university who all spoke fluent German.

The study was carried out in accordance with the recommendations of the American Psychological Association’s Ethical Principles of Psychologists as well as the rules set by the ethical guidelines of the German Psychological Society (DGPs; 2004, CIII). All participants gave written informed consent in accordance with the Declaration of Helsinki (2013) before participating, with the understanding that they could quit at any time without repercussions or drawbacks. Participants participated voluntarily and received course credits as compensation. The Regional Ethics Review Board, Bielefeld University (2020/076), approved the project.

Materials

Texts

As learning materials, we used two texts in German, adapted and shortened from prior studies (Eitel et al., 2022; Endres, Kranzdorf, et al., 2020). The texts have different content (coffee and sugar). However, they were matched in structure (three sections per text with three subsections per section), length (594 words), and readability (assessed with the Flesch Reading Ease Score; Flesch, 1948); they were of medium difficulty (coffee text: Flesch Reading Ease Score= 58; sugar text: Flesch Reading Ease Score = 54.33). Each text section had a comparable length (i. e., 198 words): The coffee text included sections on the coffee plant, coffee harvest, and coffee processing; the sugar text contained sections on societal aspects, the history of sugar, and sugar production. Each of those sections again had three subsections (66 words each). Between sections, there were no cross-references. Participants could understand each section separately. The expository texts of coffee and sugar were each divided into 27 idea units (with nine idea units per text section and three idea units per text subsection) that together indexed memory for text. The materials of the present study are available at Open Science Framework (https://osf.io/4xs8t/).

Review Tasks

In the review phase, participants were given either a retrieval or a restudy task. In the restudy task, students were asked to study the text material again for 18 min. In the retrieval task, participants were asked to answer memory questions about the text for 18 min.

The specific number of review tasks and the amount of the text (i. e., number of idea units) to be reviewed per task varied by task granularity. In the fine-grained restudy task, the entire text was divided into nine subsections using subheadings (e. g., coffee types, coffee harvest conditions) and the task was to restudy each of the subsections. In the coarse-grained restudy task, the entire text was presented without any subheadings, and participants’ single task was to restudy the entire text. In the fine-grained retrieval task, we asked nine questions per text that targeted different subsections within the text (e. g., “Please write down the knowledge you have acquired about ‘the coffee plant and its components’ as completely as possible”). In the coarse-grained retrieval task, one free-recall question was posed in which students were prompted to retrieve the entire text’s content (e. g., “Please write down the knowledge you have acquired about ‘coffee’ as completely as possible”).

Posttest

The learning outcomes were assessed by an immediate posttest at the end of each experimental session. The posttest had a medium level of granularity: Participants received three new open-answer questions, one per section of the text (e. g., “Please write down as completely as possible the knowledge you have acquired about ‘coffee plant’”). The posttest differed from the retrieval task in terms of level of task granularity (three questions vs. nine questions or one question, respectively), and as a result, the individual memory questions were new for all participants.

Self-Regulated Relearning Time

Overall self-regulated relearning time was measured as the log-transformed total number of minutes participants restudied the whole text or the text subsections, respectively, following the review phase.

Self-Efficacy

Participants were asked to rate five items on their anticipated ability to perform tasks, similar to those in the current study, but in different situational and social contexts. For example, one item asks: “Can you explain the text about sugar to a friend?” We referred to Bandura’s guidelines (2006) to construct this self-report scale. Participants marked their answers by using a scroll bar on a percentage scale (from 0 % = low to 100 % = high; Cronbach’s α = .92). This scale was administered after the experimental review phase.

Metacognitive Variables

JOL Magnitudes

After the review phase (retrieval vs. restudy tasks), we solicited JOLs using a scroll-bar (from 0 % = low to 100 % = high). Participants provided their own individual judgments of each of the nine text subsections. For each subsection, students predicted the learning outcomes in the immediate posttest. We calculated averaged subsection-specific JOL magnitudes.

Relative Retrieval–Regulation Correspondence

This score was calculated based on the relative accuracy of proportion correct at the retrieval task and relearning time (i. e., the degree to which participants’ relearning time is related to the retrieval-task performance of the corresponding text subsection). We calculated gamma correlations (Goodman & Kruskal, 1954) between retrieval-task performance and relearning time on the level of text subsections on a participant-by-participant basis and then averaged the person-specific gamma correlations across participants.

Relative JOL–Regulation Correspondence

This score was calculated based on the relative accuracy of JOLs and relearning time (i. e., the degree to which relearning time participants invested is related to the JOL magnitudes). We calculated person-specific gamma correlations between JOL magnitudes and relearning time on the level of subsections.

Motivational Variables

Mental Effort

Participants responded to a single item, asking how much effort they had invested into learning the respective text. They responded on a 9-point Likert scale using a scroll-bar (from 1 = very low to 9 = very high; see Paas, 1992; cf. Sweller, 2011). The item was presented after the initial study phase and after the review phase.

Situational Interest

To assess situational interest (Hidi & Renninger, 2006), participants rated one item indicating how interesting they found the topic in the text (from 0 = low to 9 = high).

Interindividual Difference Variables

For exploratory reasons, we also assessed academic goal orientations, prior topic interest, and self-reported prior knowledge. We assessed these variables to control for potential individual differences in the retrieval task’s benefit on immediate learning outcomes.

Self-Reported Prior Knowledge

After the posttest of each session, participants rated separately for each text subsection on a percentage scale (from 0 % = low to 100 % = high) how much of the read information they knew prior to the experiment.

Prior Topic Interest

Before the first learning phase in each session, participants rated four items on the session-specific text topics of coffee and sugar. They were asked to rate how interesting, boring, personally important, and personally useful the information on each topic was on a rating scale (from 1 = low to 5 = high; Schiefele, 1990). Participants’ answers to the boredom item were reversely coded (Cronbach’s α = .90).

Academic Goal Orientations

To assess academic goal orientations as a trait, participants rated 31 items from the student version of the scales for the Assessment of Learning and Performance Goals (SELLMO-ST; Spinath et al., 2002) on a 5-point Likert scale (from 1 = totally agree to 5 = totally disagree). It comprises scales on the following academic goal orientations: learning (Cronbach’s α = .82), performance approach (Cronbach’s α = .85), performance avoidance (Cronbach’s α = .88), and work avoidance (Cronbach’s α = .90). Each scale has eight items, except for the scale of performance-approach orientation with seven items.

Procedure

This study was conducted online using the Gorilla Experiment Builder (www.gorilla.sc; Anwyl-Irvine et al., 2019). Before the study, participants were informed about its general procedure, including two sessions (see Figure 1).

In both sessions, we used an experimental procedure for examining the benefits of retrieval tasks, as compared to restudy: (1) an initial study phase of an expository text, (2) a review phase manipulating review task (retrieval vs. restudy tasks counterbalanced across sessions), (3) a self-regulated relearning phase, and (4) an immediate posttest phase. Each experimental session lasted about 50 min.

In the initial study phase, participants were instructed to carefully study three sections of an expository text about either coffee or sugar, each section for 4 min with the expectation to be tested on it at the end of the experimental session. The presentation order of both texts was randomized (coffee text vs. sugar text in Session 1). After studying the expository text for 12 min, participants were instructed to rate their invested mental effort. The subsequent review phase – in which the experimental manipulation took place – lasted 18 min for all experimental conditions. Depending on the specific review task, participants received the instruction to either restudy or retrieve the previously learned information. In the coarse-grained retrieval task, participants were instructed to answer one global question, with no further cues, to retrieve all text contents and the connections between them (e. g., “What have you learned about ‘coffee’? Write down as completely as possible the knowledge you have acquired”); in the fine-grained retrieval task, participants were posed nine specific questions asking to provide short answers for each subsection (e. g., “What do the coffee plant and its components look like and how are they structured? Write down as completely as possible the knowledge you have acquired”). No feedback was provided after the retrieval tasks. In the coarse-grained restudy tasks, participants were instructed to study the whole text with no headings for the subsections; in the fine-grained restudy task, participants were instructed to study the nine specific subsections again provided with a specific heading for each section and subsection.

Mental effort, nine subsection-specific JOLs, situational interest, and self-efficacy were assessed after the experimental review phase. In the following self-regulated relearning phase, all participants had the opportunity to again study the text in a self-paced manner; more specifically, they were presented with nine buttons on the left-hand side labeled with the subsection headings of the learned text (i. e., coffee plant structure for the coffee text; see Figure 2); each time participants clicked on one of the buttons, the respective text in the subsections was displayed for as long as the participants wanted.

Figure 2 There were nine buttons on the left side with the nine subsection headings in the coffee text or sugar text, respectively (Panel A). If the participants clicked on a button (Panel B), the subsection text was displayed for as long as the participants wanted.

Sessions 1 and 2 were completed with the posttest including three new knowledge questions. In Session 2 (7 days after Session 1), participants went through the same procedure again, but learned from the second text. In addition, participants in the retrieval-task condition in Session 1 were assigned to the restudy-task condition for Session 2 and vice versa, to counterbalance the review tasks across sessions. At the end of Session 2, we assessed the participants’ goal orientations.

Scoring and Analyses

To assess the recall success in the retrieval tasks and the immediate learning outcomes, we scored performance (i. e., proportion of correctly recalled idea units from the text) in the retrieval tasks and in the immediate posttest. Two independent raters coded the answers of 21 participants. Both the coffee and sugar expository texts had 12 global idea units. Interrater reliability was very good both with respect to the retrieval task and the posttest (ICCs > .96). One rater continued scoring the remaining responses.

We applied an alpha level of .05 and used one-sided tests of statistical analyses in the case of directional hypotheses. To test the predictions on the Session 1 data, we conducted planned orthogonal contrast analyses (Wiens & Nilsson, 2017) specifying the following contrast weights:

•
Contrast 1 specifies the effect of the review task (retrieval vs. restudy) on the immediate learning outcomes (i. e., the learning-outcomes hypothesis): coarse-grained retrieval task (+0.5), fine-grained retrieval task (+0.5), coarse-grained restudy (–0.5), fine-grained restudy (–0.5).
•
To examine the effect of task granularity on the learning outcome, separately for the different types of review tasks, we specified a second and third contrast with the following contrast weights:
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Contrast 2 (effect of task granularity on the retrieval task): coarse-grained retrieval task (+1), fine-grained retrieval task (–1), coarse-grained restudy task (0), fine-grained restudy task (0).
•
Contrast 3 (effect of task granularity on the restudy task): coarse-grained retrieval task (0), fine-grained retrieval task (0), coarse-grained restudy task (+1), fine-grained restudy task (–1).

To examine the relevance of learning processes potentially involved, we conducted mediation analyses with process measures as mediators using a bootstrapping sample of 10,000.

Results

Table 1 provides the descriptive statistics as a function of review task and task granularity.

Table 1 Descriptive statistics of all variables as a function of review task and task granularity averaged across Sessions 1 and 2

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Retrieval-Task Performance

Performance in the retrieval task was moderate (M = .59, SD = .21). As indicated by a Welch t test, retrieval-task performance did not differ between levels of granularity, coarse-grained: M = .56, SD = .22; fine-grained: M = .62, SD = .19, t‍(101.73) = 1.33, p = 0.187, d = .26.

Relative Retrieval–Regulation Correspondence

We tested whether participants relied on prior retrieval experience in the retrieval task as a cue to self-regulate relearning time. We calculated person-specific gamma correlations between retrieval-task performance and relearning time on the level of text subsections. Note that gamma correlations could not be calculated for two participants (one participant in the coarse-grained retrieval-task condition in Session 1 and one participant in the fine-grained retrieval-task condition in Session 2) because memory performance in the retrieval task did not vary across text subsections. Overall, the relative retrieval–regulation correspondence was –.13, SD = .32), which differed significantly from 0, t‍(101) = 4.24, p < 0.001, d = .42. This result indicates that participants spent more time on text subsections associated with lower retrieval-task performance. These gamma correlations varied significantly as a function of task granularity, coarse-grained: M = –.07, SD = .34; fine-grained: M = –.20, SD = .29, t‍(100) = 2.09, p = 0.039, d = .42. Thus, fine-grained tasks led to more accurate regulation behavior.

Learning Outcomes

Exploratory ANOVA Analyses

Before we tested the specific predictions, we ran a three-way mixed-factorial ANOVA with review task, task granularity, and review-task order as a methodological factor. We found no significant effects of review task, F‍(1, 100) = 1.26, p = .265, = .002, and review-task order, F‍(1, 100) = 2.53, p = .115, = .021, on learning outcomes. However, we observed a significant interaction effect between review task and review-task order, F‍(1, 100) = 10.20, p = .002, = .014. Simple-effects analyses indicated that a retrieval-practice benefit occurred only when the retrieval task was implemented in Session 1, t‍(100) = 3.09, p = .003, but not when being implemented in Session 2 (and the restudy task in Session 1), t‍(100) = 1.45, p = .151. Notably, the performance levels of the restudy task posttest remained similar irrespective of the review-task order, t‍(100) = 0.30, p = .765, while the retrieval task performance level in Session 2 was lower compared to Session 1, t‍(100) = 2.74, p = .007, resulting in an insignificant retrieval-task effect when the restudy task was implemented first (see Figure 3).

As there is a moderating effect of review-task order on the benefit of retrieval practice on the immediate learning outcome, we cannot exclude potential transfer effects from restudy to retrieval tasks or vice versa both in terms of learning strategies and motivational effects.

Second, we examined the granularity effects of the review tasks. These results revealed neither a main effect, F‍(1, 100) = 0.16, p = .688, = .001, nor any interaction effects including this factor, ps > .45.

**Figure 3 Immediate learning outcomes as a function of review task and session.**

Contrast Analyses

To measure the retrieval-practice effect without potential moderating effects of review-task order (e. g., in terms of learning strategies and motivational effects), we tested our hypotheses via contrast analyses of the data from Session 1 (for descriptive statistics as a function of review task and task granularity, see Table 2). This is line with prior research that primarily investigated the effects of review task on relearning on a between-subjects level, but failed to provide any robust evidence on retrieval-task benefits using a within-subjects design due to potential order effects of the review tasks (Soderstrom & Bjork, 2014).

Table 2 Descriptive statistics of all variables as a function of review task and task granularity in Session 1

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In the learning-outcome hypothesis, we predicted that a retrieval task enhances the immediate learning outcomes more than restudy. We conducted three planned orthogonal contrasts on posttest performance using Session 1 data on a between-subjects level. We found a significant immediate benefit of retrieval task, t‍(100) = 2.10, p = .039 (see Figure 3). Exploratory analyses showed that this retrieval-task effect was not moderated by prior knowledge, prior topic interest, or goal orientations, ps > .10. To test potential differences related to task granularity, we conducted two orthogonal contrast analyses. We did not find a significant granularity effect on learning outcomes, neither for the retrieval task, t‍(100) = .02, p = .983, nor for the restudy task, t‍(100) = 1.02, p = .309.

Mediational Analyses

On the basis of our hypotheses, we examined the potential metacognitive and motivational mechanisms of retrieval-task benefits on immediate learning outcomes. Given that task granularity did not significantly influence learning outcomes, we collapsed the data across levels of this factor.

Relearning Time

We predicted that the retrieval task increases the overall self-regulated relearning time. This regulation hypothesis was supported by an orthogonal contrast analysis, showing a significant benefit of retrieval task on relearning time, t‍(87.83) = 3.59, p < .001, d = .71. Students displayed longer relearning times after a retrieval task than after a restudy task.

In the regulation–mediator hypothesis, we predicted that the retrieval task in part enhances the learning outcomes via increased relearning time. However, a mediation analysis failed to reveal any indirect effect of review task on the learning outcomes via relearning time, β = 0.133, 95 % CI [−0.028, 0.328].

Self-Efficacy

A contrast analysis revealed an effect of review task on self-efficacy, t‍(102) = 2.59, p = .011, d = .51, indicating that self-efficacy was lower after the retrieval task. A mediation model revealed the indirect effect of the review task on the learning outcomes via self-efficacy, β = −0.139, 95 % CI [−0.301, −0.027]. We did not find any serial indirect effect via self-efficacy and relearning time, β = −0.009, 95 % CI [−0.046, 0.007].

Metacognition

The monitoring hypothesis states that JOL magnitudes are lower after a retrieval task than after a restudy task. A contrast analysis revealed a significant effect of review task, t(102) = 4.18, p < .001, d = .82, with JOL magnitudes being lower for a retrieval task compared to a restudy task.

In the monitoring–regulation–mediator hypothesis, we predicted that a retrieval task, compared to a restudy task, enhances the learning outcomes by lower JOL magnitudes and subsequently increased relearning time. Mediation analyses revealed a retrieval task’s indirect effect on learning outcomes via JOL magnitudes, β = −0.246, 95 % CI [−0.465, −0.085]. We did not find any serial indirect effect via JOL magnitudes and relearning time, β = −0.018, 95 % CI [−0.082, 0.009] (see Figure 4).

Figure 4 Multiple and serial mediation model illustrating the effect of review task on the learning outcomes in the immediate posttest via JOL magnitude and relearning time. JOL = judgments of learning.

We examined the prediction that participants’ monitoring judgments are related to the relearning times. To measure the relative JOL–regulation correspondence, we calculated averaged, person-specific gamma correlations between JOL magnitudes and relearning times on the individual level of text subsections. Note that gamma correlations as an index for JOL–regulation correspondence could not be calculated for three participants in Session 1 (n = 2 in the coarse-grained restudy group; n = 1 participant in the fine-grained restudy group) because there was no variation in JOL magnitudes (i.e., JOL magnitudes reached an average proportion of 1 or 0). Overall, the relative JOL–regulation correspondence was –.10 (SD = .29), which differed significantly from 0, t(100) = 3.57, p < 0.001, d = .36. That is, participants spend more time on text subsections associated with lower JOL magnitudes. Importantly, to test the relative JOL–regulation–correspondence hypothesis,we examined whether the gamma correlations varied as a function of review task. A contrast analysis showed that the gamma correlation was significantly higher for the retrieval task (M = −.15, SD = .28), compared to the restudy task (M = −.05, SD = .29), t(99) = 1.73, p = .043, d = .34 (one-sided test). However, an additional mediation analysis did not reveal an indirect effect of review task on learning outcomes via relative JOL–regulation correspondence, β =.048, 95% CI [−0.018, 0.181].

Motivation

We predicted that a retrieval task enhances motivational variables more than a restudy task. To investigate the motivation hypothesis, an orthogonal contrast analysis was conducted. We found a significant effect of retrieval task on mental effort, t‍(75.92) = 6.41, p < .001, d = .34. We did not find a significant effect on situational interest, t‍(102) = 0.95, p = .173, d = .19 (one-sided tests). Thus, students exhibited higher mental effort but similar levels of situational interest after the retrieval task.

The motivation–mediator hypothesis states that a retrieval task enhances the learning outcomes via motivational variables (i. e., mental effort and situational interest). A multiple mediation model did not show an indirect effect of the review task on the learning outcomes via neither mental effort, β = −0.030, 95 % CI [−0.225, 0.303], nor situational interest, β = 0.006, 95 % CI [−0.049, 0.072].

Discussion

In this study, we demonstrated how retrieval practice can enhance self-regulated relearning and immediate learning outcomes when learning complex text materials. We investigated the potentially underlying motivational and metacognitive mechanisms of this immediate effect of retrieval practice. More specifically, we showed that JOL magnitudes (a metacognitive measure) and self-efficacy (likely a mixture of both metacognitive and motivational aspects) mediated the retrieval-based learning benefit on the learning outcome. Both metacognitive and motivational mechanisms should be examined together to explain potential retrieval-practice effects on immediate learning outcomes in the context of self-regulated relearning.

Do Retrieval Tasks Enhance Self-Regulated Relearning and Posttest Performance?

When providing a relearning opportunity, we found an immediate retrieval-practice effect in Session 1 (i. e., the learning-outcome hypothesis). However, we observed no clear evidence of a learning benefit from retrieval tasks at the within-subjects level. This result is consistent with prior work on pair-associative learning demonstrating an immediate retrieval-practice effect in a between-subjects, but not in a within-subjects, design (Soderstrom & Bjork, 2014). More specifically, we found that retrieval-task order affected the retrieval practice effect: A retrieval task improved the immediate learning outcome when it was provided in Session 1, but not when the restudy task was provided in Session 1. Although we separated review tasks with a 1-week delay, we observed that the review-task order moderated the effect of retrieval task on the learning outcome and that the immediate retrieval-task benefit was only existent in Session 1 when potential effects of review-task order were not existent. A tentative explanation for this results pattern is that restudy reduced the motivation to engage in the retrieval task in Session 2, or the retrieval task enhanced the motivation or learning strategies of the restudy task in Session 2, compared to no prior review task. However, on the basis of our incomplete design to detect transfer effects regarding prior review tasks, we cannot draw any firm conclusions about the specific nature of this finding. To eliminate potential influences of review-task order, we tested our hypotheses regarding retrieval practice effects and its potentially underlying mechanisms with data on Session 1.

We used an experimental design with unfavorable boundary conditions of the direct retrieval-practice effect, thereby maximizing the relative contribution of the indirect retrieval-practice effect. First, we reduced the retention interval to a few minutes, as there is evidence that you need a longer delay to observe a direct retrieval-practice effect (for meta-analytic evidence see, e. g., Rowland, 2014, and Yang et al., 2021). For short delays, restudy tasks are often more effective than retrieval tasks (Kubik et al., 2018), especially for complex materials (Rummer & Schweppe, 2022). Second, to observe a direct benefit from retrieval tasks (without feedback), success in the retrieval task needs to be high. Rowland’s (2014) meta-analysis showed a substantial direct retrieval-practice effect only when the retrieval-task success is approximately 75 %; retrieving information strengthens its representation in memory, but the information must be successfully recalled for this to happen. The retrieval-task success rate in the present study was only 59 %. Although we cannot rule out a direct retrieval-practice effect, much of the learning benefits we observed are likely due to the indirect retrieval-practice effect; we compared the benefits of retrieval versus restudy tasks on posttest performance after a short delay, while the retrieval-task performance was moderate. Implementing these unfavorable conditions for the direct benefits of retrieval tasks allowed us to assume that indirect retrieval-practice effects largely contribute to the immediate learning benefit of the retrieval task in Session 1. On a mechanistic level, we speculate that indirect retrieval-practice effects may rely on a partially different set of mechanisms than the direct retrieval-practice effect. For the latter, retrieval success (e. g., Rowland, 2014) and invested mental effort (e. g., Carpenter, 2009; Endres & Renkl, 2015; Pyc & Rawson, 2009) have been shown to be key mediator variables. In this study, in which indirect retrieval-practice effects had assumedly a larger impact, neither retrieval success nor mental effort played a role for the immediate learning benefits. Disentangling these two effects of retrieval practice would require a more complex experimental design that also manipulates the presence of the subsequent relearning phase (see Arnold & McDermott, 2013; Wissman & Rawson, 2018). Further research should focus on a comprehensive theory explaining both direct and indirect effects of retrieval tasks, also in terms of elucidating metacognitive and motivational mechanisms.

Mechanisms Underlying the Benefit of Retrieval Tasks on Self-Regulated Relearning

We examined the motivational and metacognitive mechanisms involved in the immediate learning benefit observed in Session 1. Self-efficacy relates to an individual’s confidence in their abilities for a task and most likely involves both metacognitive and motivational aspects. Based on our findings, retrieval practice seems to reduce such confidence, similar to JOL magnitudes, and this decrease in self-efficacy mediated the retrieval practice effects. However, there was no indirect effect of retrieval tasks via self-efficacy and self-regulated relearning time on the learning outcome.

Metacognitive Mechanisms

With respect to our hypotheses on metacognition, we identified a significant decrease in JOL magnitudes after retrieval compared to restudy (monitoring hypothesis), which mediated the effect of the review task on posttest performance (monitoring–mediator hypothesis). This finding is consistent with the idea that accurate metacognitive monitoring is critical for effective self-regulated relearning. Accurate metacognitive monitoring is thought to improve the learning outcome via effective self-regulated relearning (e. g., Metcalfe, 2009). However, the significant effect of the review task on overall relearning time (regulation hypothesis) did not mediate the benefit of retrieval tasks on the posttest performance (regulation–mediator hypothesis). This finding is consistent with prior research on text learning, suggesting that retrieval tasks improve the learning outcome not by the total amount of time students spend on self-regulated relearning after initial review, but how they allocate their relearning time to different topics (Thiede et al., 2003). The labor-in-vain effect refers to the situation where students invest effort (or self-regulated relearning time) in learning that does not help them to retrieve more after the subsequent relearning phase (Nelson & Leonesio, 1988). Low relative monitoring accuracy, indicating that students cannot differentiate between topics they know better or worse, can lead to a labor-in-vain effect.

When the metacognitive variables were examined at the level of text subsections, we found that the review task influenced the quality of regulatory learning behavior, as indicated by gamma correlations. As predicted, the results showed that participants spent more time relearning text subsections, which was associated with lower retrieval-task performance. Fine-grained, compared to coarse-grained, retrieval tasks enhanced this relative retrieval–regulation correspondence, which is consistent with previous findings on the effect of task granularity on metacognitive accuracy (e. g., Endres, Kranzdorf, et al., 2020). Those benefits on regulation, however, did not translate into an improved immediate learning outcome. Similarly, as predicted, participants spent more time relearning text sections associated with low JOL magnitudes and, in support of the relative JOL–regulation correspondence hypothesis, retrieval tasks increased this JOL–regulation correspondence. This improved regulation also did not translate into improved immediate learning outcomes.

Our findings on metacognitive regulation highlight that regulation behavior should not be assessed merely on a global level in terms of overall self-regulated relearning time, but in a more fine-grained way. It is the assessment of the specific allocation of relearning time that is important for gaining insights into students’ subsequent relearning behavior. Given that even a fine-grained analysis of regulation time did not predict learning outcomes, future research should not focus solely on the quantity of relearning time spent. It is equally important to examine the quality of learning strategies employed by students during subsequent relearning, because these learning strategies can be influenced by retrieval tasks. Rather than changing the duration of their relearning time, some students may instead alter the type and quality of learning strategies they employ during self-regulated relearning. The choice of applied learning strategies typically predicts future learning outcomes (Endres et al., 2017; Roelle, Schweppe, et al., 2022).

Motivational Mechanisms

Hypotheses on the role of increased motivation were only partially supported. We found that the retrieval tasks increased the level of mental effort, but not of situational interest (motivation hypothesis). Furthermore, mental effort did not mediate the effect of the retrieval task on posttest performance (motivation–mediator hypothesis). Although mental effort and situational interest are both motivational measures, they capture different aspects of motivation in general and of the observed motivational benefits of retrieval tasks. Mental effort was increased by retrieval tasks; however, this increase did not mediate the immediate learning benefits. We found no significant association between mental effort after the review phase and the learning outcome on the posttest (r = 0.110, p > .10). It is unclear why mental effort after the review phase was not a significant predictor of the learning outcome. This missing link might be due to the mixed motivational and cognitive nature of the mental-effort item (Paas, 1992; see Klepsch & Seufert, 2021).

The missing effect of review task on situational interest might be due to our rather coarse assessment of situational interest with only one item. Situational interest is a complex construct that involves the feeling and value that a learning task can evoke about the learning material at hand. Hence, the single-item assessment in this study might have limited validity. In future studies, situational interest should be assessed with more differentiated and refined scales (e. g., Endres, Weyrether, et al., 2020; Renninger & Hidi, 2022). Another possible explanation for our findings is that retrieval practice can enhance mental effort, which is more closely related to the task format. Situational interest, on the other hand, may be more closely related to the topic itself (i. e., maintaining situational interest; Hidi & Renniger, 2006). A retrieval attempt may have the potential to trigger task-related motivation that does not necessarily increase the more global topic-related situational interest. Perhaps increased task-related motivation is sufficient to improve learning, even if topic-related situational interest is relatively low. Future research should examine the possibility that an additional intervention to increase situational interest may enhance the motivationally mediated indirect learning benefits of retrieval practice.

Future research should further investigate both metacognitive and motivational mechanisms that may underlie retrieval-practice effects on immediate learning outcomes when learners receive the opportunity for self-regulated relearning of complex learning materials. Examining individual mechanisms in isolation may provide an oversimplified picture of the mechanisms underlying retrieval-task effects on self-regulated relearning and immediate learning outcomes. More nuanced measures of the nature of relearning decisions would further elucidate the motivational underpinnings of retrieval-task effects. For example, think-aloud protocols could provide insights into why students choose a particular learning strategy and why they relearn a text for as long as they do. By assessing deliberate study decisions in this way, nonlinear relations between metacognitive monitoring and metacognitive regulation can be identified. For instance, students may prioritize relearning texts that are moderately well learned without spending much time relearning texts that seem to be “unlearnable” or are already well understood (e. g., Metcalfe, 2002).

Practical Implications

We examined the effect of retrieval practice on self-regulated relearning of complex text materials and immediate learning outcomes. The present findings can be applied to educational settings in schools (Richter et al., 2022). For instructors, these results are relevant in that retrieval practice can increase metacognitive monitoring accuracy, and thus have immediate positive effects on future learning behaviors and outcomes. These findings support the expectation that when immediate learning gains are the primary learning goal, retrieval practice is the preferred review activity compared to restudy. Future research should provide more insight into how retrieval tasks can be instructionally supported to realize their full potential in educational practice.

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Special Issue: Advances in Understanding and Fostering the Accuracy of Metacognitive Judgments

Volume 55Issue 2-3April 2023

ISSN: 0049-8637eISSN: 2190-6262

Licenses & Copyright

Distributed as a Hogrefe OpenMind article under the license CC BY-ND 4.0 ( https://creativecommons.org/licenses/by-nd/4.0)

Keywords

Authorship:

Tino Endres and Veit Kubik contributed equally to this publication; both should be considered as shared first authors.

Open Data:

The materials of the present study are available at Open Science Framework (https://osf.io/4xs8t/).

PDF download

Funding:

Open access publication enabled by University of Freiburg.

	All		Restudy Task				Retrieval Task
			Fine-Grained		Coarse-Grained		Fine-Grained		Coarse-Grained
Variables	M	SD	M	SD	M	SD	M	SD	M	SD
Learning Outcome Variables
Retrieval-Task Performance	0.59	0.21	–	–	–	–	0.62	0.19	0.56	0.22
Posttest Performance	0.67	0.24	0.67	0.25	0.65	0.25	0.69	0.24	0.67	0.24
Relearning Time (log)	1.11	1.28	0.82	1.49	0.97	1.21	1.34	1.02	1.30	1.32
Self-Efficacy	58.56	20.98	63.49	18.42	60.59	21.81	57.52	21.45	53.08	21.04
Metacognitive Variables
JOL Magnitude	57.48	21.47	63.54	20.63	62.79	20.85	52.05	19.24	51.60	22.37
JOL–Regulation Correspondence	–0.10	0.30	–0.01	0.32	–0.11	0.27	–0.14	0.26	–0.12	0.32
Retrieval–Regulation Correspondence	–0.13	0.32	–	–	–	–	–0.20	0.29	–0.07	0.34
Motivational Variables
Mental Effort After Initial Study	6.30	2.00	5.78	2.22	6.65	1.72	6.39	1.92	6.35	2.07
Mental Effort After Review Task	6.53	2.05	5.24	2.21	5.89	2.01	7.45	1.34	7.49	1.59
Situational Interest	4.64	2.55	4.45	2.62	4.22	2.75	4.98	2.54	4.91	2.28
Interindividual Difference Variables
Age	26.59	11.62	27.59	12.65	25.69	10.71	27.59	12.65	25.69	10.71
Prior Knowledge	22.26	21.85	24.88	25.41	19.35	19.40	26.46	25.69	19.11	15.89
Prior Topic Interest	3.01	1.09	2.96	1.18	2.69	1.08	3.13	1.08	3.25	0.97
SELLMO-ST Scales
Learning	48.39	5.50	48.16	5.47	48.60	5.60	48.16	5.47	48.60	5.60
Performance Approach	28.68	7.96	27.82	7.65	29.45	8.26	27.82	7.65	29.45	8.26
Performance Avoidance	23.95	8.82	23.10	8.48	24.71	9.16	23.10	8.48	24.71	9.16
Work Avoidance	19.73	8.52	20.06	8.55	19.44	8.60	20.06	8.55	19.44	8.60

	All		Restudy Task				Retrieval Task
			Fine-Grained		Coarse-Grained		Fine-Grained		Coarse-Grained
Variables	M	SD	M	SD	M	SD	M	SD	M	SD
Learning Outcome Variables
Retrieval-Task Performance	0.62	0.19	–	–	–	–	0.64	0.15	0.60	0.21
Posttest Performance	0.70	0.22	0.68	0.23	0.62	0.25	0.74	0.19	0.74	0.20
Relearning Time (log)	1.17	1.08	0.59	1.25	0.97	1.15	1.56	0.80	1.50	0.85
Self-Efficacy	61.92	18.73	68.43	14.39	65.19	19.50	58.51	20.43	56.28	18.00
Metacognitive Variables
JOL Magnitude	62.60	20.28	73.14	14.64	68.27	19.66	52.72	19.03	57.26	20.94
JOL–Regulation Correspondence	–0.10	0.29	–0.03	0.32	–0.07	0.28	–0.16	0.26	–0.13	0.30
Retrieval–Regulation Correspondence	–0.20	0.33	–	–	–	–	–0.26	0.32	–0.15	0.34
Motivational Variables
Mental Effort After Initial Study	6.78	2.00	5.87	2.87	6.71	1.84	7.04	1.68	7.37	1.18
Mental Effort After Review Task	6.75	2.01	5.30	2.30	5.93	1.98	7.69	1.16	7.93	1.14
Situational Interest	4.70	2.65	4.83	2.66	4.14	2.84	4.46	2.76	5.41	2.27
Interindividual Difference Variables
Age	26.59	11.64	29.78	13.25	26.18	11.72	25.65	12.02	25.19	9.74
Prior Knowledge	21.33	21.92	33.16	31.81	18.68	18.05	16.63	17.91	18.53	15.46
Prior Topic Interest	2.97	1.17	3.03	1.28	2.59	1.17	3.02	1.17	3.26	0.99
SELLMO-ST Scales
Learning	48.39	5.52	49.83	3.52	48.50	5.62	46.69	6.46	48.70	5.68
Performance Approach	28.68	7.98	27.87	7.99	27.46	9.35	27.77	7.49	31.52	6.49
Performance Avoidance	23.95	8.84	22.83	7.91	23.68	8.43	23.35	9.11	25.78	9.91
Work Avoidance	19.73	8.54	20.57	8.67	18.79	8.63	19.62	8.60	20.11	8.67

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Immediate Learning Benefits of Retrieval Tasks

On the Role of Self-Regulated Relearning, Metacognition, and Motivation

Abstract

Benefits of Retrieval Tasks

Retrieval Tasks Influence Self-Regulated Relearning

Retrieval Tasks Enhance Metacognitionand Motivation

Metacognition

Motivation

Benefits of Retrieval Tasks

On the Moderating Role of Task Granularity

The Present Study

Method

Design and Sample

Materials

Texts

Review Tasks

Posttest

Self-Regulated Relearning Time

Self-Efficacy

Metacognitive Variables

JOL Magnitudes

Relative Retrieval–Regulation Correspondence

Relative JOL–Regulation Correspondence

Motivational Variables

Mental Effort

Situational Interest

Interindividual Difference Variables

Self-Reported Prior Knowledge

Prior Topic Interest

Academic Goal Orientations

Procedure

Scoring and Analyses

Results

Retrieval-Task Performance

Relative Retrieval–Regulation Correspondence

Learning Outcomes

Exploratory ANOVA Analyses

Contrast Analyses

Mediational Analyses

Relearning Time

Self-Efficacy

Metacognition

Motivation

Discussion

Do Retrieval Tasks Enhance Self-Regulated Relearning and Posttest Performance?

Mechanisms Underlying the Benefit of Retrieval Tasks on Self-Regulated Relearning

Metacognitive Mechanisms

Motivational Mechanisms

Practical Implications

Literatur

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