Bibliographic record
Abstract
Developing learners’ ability to self-regulate their own learning has been an ideal sought after by researchers and practitioners alike. Over the past 40 years a plethora of educational psychology research on self-regulated learning (SRL) has flooded the literature. In this article I attempt to consolidate key theories from this literature base and propose a 6-point strategy to CREATE a culture of SRL. I will argue that instructors must communicate proximal and long-term goals that have been negotiated by a community of learners, substantially reward all positive aspects of the learning process, judge and reward the learning process by assigning elaborative learning assessments, realistically attribute success and failure to appropriate processes, and tune ineffective strategies and goals to allow the curriculum to evolve in accordance with learner difficulty and success. Enseigner aux apprenants la capacité d’autoréguler leur propre apprentissage est un idéal que poursuivent les chercheurs et les praticiens. Au cours des quarante dernières années, il y a eu surabondance d’études sur l’autorégulation de l’apprentissage dans la littérature sur la psychologie de l’éducation. Dans cet article, l’auteur tente de regrouper les principales théories tirées de cette documentation et de proposer une stratégie composée de six éléments contribuant à la création d’une culture de l’autorégulation. Il soutient que les enseignants doivent communiquer les objectifs à court et à long terme convenus par les apprenants, reconnaître de façon marquée tous aspects positifs du processus d’apprentissage, évaluer et reconnaître ce processus en donnant des travaux élaborés afin de mesurer l’apprentissage, attribuer de façon réaliste les succès et les échecs aux processus appropriés et adapter les stratégies et buts inefficaces afin de permettre l’évolution du curriculum en tenant compte des difficultés et des réussites des apprenants.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".