A Metacognitive Instructional Guide to Support Effective Studying Strategies
Bibliographic record
Abstract
Metacognition—the processes whereby learners assess and monitor their progress in learning (metacognitive monitoring, MM) and use these judgements of learning to make choices about what to study in the future (metacognitive control, MC)—has been shown to be beneficial to learning. However, effective learning also relies on metacognitive knowledge (MK)—that is, students’ knowledge about effective study strategies and how to employ them. Few students receive explicit in-class instruction on these topics. Here, we explore if an online instructional guide, which includes information about evidence-based study strategies, example questions for self-testing, and a study calendar to help regulate timing of studying can effectively teach MK to improve performance. While it is unclear if the online instructional guide was related to increases in MK, MM, and MC, we did observe benefits to student performance, particularly in highly anxious students on high-stake assessments such as the final examination. Future research should seek to understand how students were engaging with the guide and how the nature of the engagement impacted their study strategies.Il a été montré que la métacognition – les processus par lesquels les apprenants et les apprenantes évaluent et suivent leurs progrès en apprentissage (surveillance métacognitive) et utilisent ces jugements d’apprentissage pour faire des choix concernant ce qu’ils veulent étudier à l’avenir (contrôle métacognitif) – est bénéfique à l’apprentissage. Toutefois, l’apprentissage efficace s’appuie également sur la connaissance métacognitive, c’est-à-dire sur le fait que les étudiants et les étudiantes connaissent les stratégies d’études efficaces et savent les employer. Peu d’étudiants et d’étudiantes reçoivent des directives explicites en classe sur ces sujets. Dans cet article, nous tentons de voir si un guide d’instruction en ligne, qui comprend des informations sur des stratégies d’études fondées sur des données probantes, des questions pour effectuer des auto-évaluations, ainsi qu’un calendrier d’apprentissage pour régulariser l’emploi du temps des études, peut effectivement enseigner la connaissance métacognitive afin d’améliorer les résultats.Bien qu’il ne soit pas clair si le guide d’instruction en ligne était relié aux augmentations en matière de connaissance métacognitive, de surveillance métacognitive et de contrôle métacognitif, nous avons toutefois observé des avantages dans les résultats des étudiants et des étudiantes, en particulier parmi ceux et celles qui souffrent fortement d’anxiété quand ils et elles doivent faire des travaux importants tels que les examens finaux. Des recherches futures devraient chercher à comprendre comment les étudiants et les étudiantes avaient utilisé le guide et comment la nature de leur engagement avait affecté leurs stratégies d’études.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".