The Relation Between Students’ Effort and Monitoring Judgments During Learning: A Meta-analysis
Open accessAbstract
Research has shown a bi-directional association between the (perceived) amount of invested effort to learn or retrieve information (e.g., time, mental effort) and metacognitive monitoring judgments. The direction of this association likely depends on how learners allocate their effort. In self-paced learning, effort allocation is usually data driven, where the ease of memorizing is used as a cue, resulting in a negative correlation between effort and monitoring judgments. Effort allocation is goal driven when it is strategically invested (e.g., based on the importance of items or time pressure) and likely results in a positive correlation. The current study used a meta-analytic approach to synthesize the results from several studies on the relationship between effort and monitoring judgments. The results showed that there was a negative association between effort and monitoring judgments ( r = − .355). Furthermore, an exploration of possible moderators of this association between effort and monitoring was made. The negative association was no longer significant when goal-driven regulation was manipulated. Furthermore, it was found that the type of monitoring judgment (i.e., a weaker association for prospective judgments) and type of task (stronger association for problem-solving tasks relative to paired associates) moderated the relation between effort and monitoring. These results have important implications for future research on the use of effort as a cue for monitoring in self-regulated learning.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The negative correlation between effort ratings and judgments of learning is weaker for prospective than retrospective judgments, and stronger for problem-solving than paired associates tasks.
“The correlation was weaker for prospective than for retrospective judgments, and stronger for problem-solving than for paired associates tasks.”
From Worth the Effort: the Start and Stick to Desirable Difficulties (S2D2) Framework (2023) · cites this paper as Baars et al., 2020
The Main Findings report a stronger negative correlation for problem-solving than for paired-associates tasks. They also report a weaker correlation for prospective than for concurrent judgments, not retrospective judgments. The paper distinguishes concurrent from retrospective judgments in “The Types of Monitoring Judgments,” so the stated timing comparison is not supported. (LLM, conf=0.91)
Experimental — accuracy not yet measuredIn the Baars et al. (2020) meta-analysis, the subset of studies where learners adopted a goal-driven interpretation of effort showed no negative correlation between effort and learning.
“In the Baars et al. (2020) meta-analysis, the subset of studies where learners adopted a goal-driven interpretation of effort showed no negative correlation between effort and learning.”
From Worth the Effort: the Start and Stick to Desirable Difficulties (S2D2) Framework (2023) · cites this paper as Baars et al., 2020
The Abstract and Main Findings report that the negative association became nonsignificant under some goal-driven manipulations. But the measured association was between effort and monitoring judgments, not effort and learning outcomes. The Moderators section also says that, when goal-driven manipulations were pooled, the correlation remained significantly negative; it became nonsignificant in the separate incentives and time-pressure analyses. (LLM, conf=0.88)
Experimental — accuracy not yet measuredProviding lengthy comments in peer feedback benefits reviewers.
“Providing lengthy comments in peer feedback has been suggested to also benefit reviewers Zhu and Carless (2018), as it may indicate greater effort put into reviews, contributing to learning and self-regulation (Baars, Wijnia, de Bruin, & Paas, 2020).”
From Impact of AI assistance on student agency (2024) · cites this paper as Baars, Wijnia, de Bruin, & Paas, 2020
The Discussion links students’ monitoring judgments to regulation and learning outcomes, and Data-Driven and Goal-Driven Self-Regulation describes effort as a cue in self-regulated learning. Baars et al. do not address peer-feedback comments, whether longer comments indicate greater review effort, or whether that effort benefits reviewers. The sentence cites Zhu and Carless, rather than Baars et al., for the suggested reviewer benefit. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTime measure can serve as an indicator of students' commitment to the feedback task and their potential for self-regulation.
“Therefore, the time measure can serve as an indicator of students ' commitment to the feedback task and their potential for self-regulation.”
From Impact of AI assistance on student agency (2024) · cites this paper as Baars et al., 2020
The Effort Measurement section treats logged study time as an objective measure of learning effort, and Data-Driven and Goal-Driven Self-Regulation links time allocation with self-regulation. The paper does not examine feedback tasks or establish that time spent on one indicates commitment to feedback or a student's potential for self-regulation. No page number is available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe goal-driven approach is less common among learners than the data-driven approach.
“The Baars etal.'s (2020) meta-analysis shows that the goal-driven approach is less common among learners than the data-driven approach.”
From Worth the Effort: the Start and Stick to Desirable Difficulties (S2D2) Framework (2023) · cites this paper as Baars et al., 2020
The Data-Driven and Goal-Driven Self-Regulation section describes both approaches but does not compare how commonly learners use them. Limitations and Future Studies says only a small number of included studies manipulated goal-driven self-regulation; that is a statement about the studies, not the prevalence of the approach among learners. Searches for a learner-level frequency comparison found none. (LLM, conf=0.73)
Experimental — accuracy not yet measured
Longer comments have been associated with enhanced learning and self-regulation.
“which have been associated with enhanced learning and self-regulation (Baars et al., 2020; Cavalcanti et al., 2020; Osakwe et al., 2021; Zhu & Carless, 2018).”
From Impact of AI assistance on student agency (2024) · cites this paper as Baars et al., 2020
The Discussion and Data-Driven and Goal-Driven Self-Regulation sections discuss learning effort, monitoring judgments, and self-regulation, but do not address comment length. Searches for longer comments and feedback comments found no passage linking them to enhanced learning or self-regulation. Page numbers were not available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredProviding lengthy comments, regardless of their quality, can benefit the reviewer and contribute to learning and self-regulation.
“as previous research found that providing lengthy comments, regardless of their quality, can benefit the reviewer and contribute to learning and self-regulation Zhu and Carless (2018); Baars et al. (2020).”
From Impact of AI assistance on student agency (2024) · cites this paper as Baars et al. (2020)
The Discussion (page unavailable) describes a meta-analysis of students’ effort and monitoring judgments during learning, not the effects of providing comments. Searches for comment length, comment quality, reviewer benefits, and peer feedback found no passage addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredStudents might perceive that having to try hard, that is, expending a great deal of effort, is indicative of a lack of academic ability.
“Students might perceive that having to try hard (i.e., expending a great deal of effort) is indicative of a lack of academic ability (Baars et al., 2020).”
From Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda (2022) · cites this paper as Baars et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 73 · Cited by 101
- Authors
- Baars, M., Wijnia, L., de Bruin, A., & Paas, F.
- Affiliations
- Erasmus University Rotterdam (NL)
- HZ University of Applied Sciences (NL)
- University of Wollongong (AU)
- Corresponding author
- Baars, Martine
- Venue
- Educational Psychology Review
- Published
- 2020-09-09
- Volume
- 32
- Issue
- 4
- Pages
- 979-1002
- ISSN
- 1040-726X (ISSN-L)
- 1573-336X
- OpenAlex
- W3086595731
- Cited by
- 101
- References
- 73
- Citations by year
- 2026: 16 · 2025: 14 · 2024: 18 · 2023: 22 · 2022: 14 · 2021: 9 · 2020: 6 · 2019: 1
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Educational Psychology Review (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, cc-by)
- RePub (Erasmus University Rotterdam) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Erasmus University
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- metacognitive monitoring, effort allocation, student self-regulation, data-driven regulation, problem-solving tasks, paired associates, learning effort
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
