Encouraging Self-Regulated Learning Through Electronic Portfolios
Bibliographic record
Abstract
At the Centre for the Study of Learning and Performance (CSLP) at Concordia University in Montreal, Quebec, we have developed the Electronic Portfolio Encouraging Active Reflective Learning Software (ePEARL) to promote student self-regulation and enhance student core competencies. This paper summarizes the literature on electronic portfolios (EPs), describes ePEARL, and documents our research findings to date including analyses of teacher and student reactions. Participants in this study were 62 school teachers, mostly from elementary schools, and their students (approximately 1200) from seven urban and rural English school boards across Quebec. Student and teacher post-test questionnaire responses suggested that the use of portfolios, and the learning processes they support, were positively viewed and learned well enough to be emerging skills among students. Contrariwise, teachers commented that teaching SRL strategies was new and thus required a change in teaching strategies, strategies that they were not yet accustomed to. Focus groups also revealed the challenges of using portfolios to teach children to self-regulate. And finally, the analysis of student portfolios evidenced only small amounts of student work or high levels of student self-regulation. Resume : Au Centre d’etudes sur l’apprentissage et la performance (CEAP) de l’Universite Concordia a Montreal, Quebec, nous avons concu le logiciel de portfolio electronique reflexif pour l’apprentissage des eleves (PERLE) afin d’encourager l’apprentissage autoregule chez les eleves et d’accroitre leurs competences de base. Cet article presente un resume de la documentation sur les portfolios electroniques, une description de PERLE, ainsi que nos resultats de recherche documentes a ce jour, y compris des analyses des reponses des enseignants et des eleves. Les participants a cette etude se composaient de 62 enseignants, la plupart dans des ecoles primaires, et de leurs eleves (environ 1200) provenant de sept commissions scolaires anglophones urbaines et rurales du Quebec. Les reponses des eleves et des enseignants au posttest suggerent que les portfolios et les processus d’apprentissage qu’ils soutiennent ont ete percus de maniere positive et qu’ils ont ete suffisamment assimiles pour se traduire par de nouvelles competences chez les eleves. En revanche, les enseignants ont mentionne qu’enseigner les strategies d’apprentissage autoregule etait nouveau et que cela exigeait de modifier leurs strategies d’enseignement pour en adopter d’autres auxquelles ils n’etaient pas encore habitues. Les groupes de discussion ont egalement fait ressortir les defis lies a l’utilisation des portfolios dans le but d’apprendre l’autoregulation aux enfants. Enfin, l’analyse des portfolios des eleves a revele que seulement une petite portion des travaux d’eleves demontrait des niveaux eleves d’autoregulation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".