Using electronic portfolios to foster literacy and self-regulated learning skills in elementary students.
Bibliographic record
Abstract
The research presented here is a continuation of a line of inquiry that explores the impacts of an electronic portfolio software called ePEARL, which is a knowledge tool designed to support the key phases of self-regulated learning (SRL)—forethought, performance, and self-reflection—and promote student learning. Participants in this study were 21 teachers from elementary schools (Grades 4–6) and their students (N = 319) from 9 urban and rural English school boards in Quebec and Alberta, Canada, who participated during the 2008–2009 school year. Students with low enthusiasm for the use of ePEARL were excluded from the main sample as they exhibited different patterns in learning gains and self-regulatory skills as compared with those with high and medium enthusiasm. Multivariate analyses of covariance showed that students motivated to use the software made significantly greater gains compared with controls in 3 of 4 writing and reading skills (p < .01) as assessed by the constructed response subtest of the Canadian Achievement Test (fourth edition). Multivariate analyses of covariance of student survey data revealed that, over time, students who used the software reported higher levels of SRL processes than those in the control group (p < .01). Implications of the findings for school leaders and teacher educators regarding the use of electronic portfolios are discussed.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".