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Record W4283581579 · doi:10.29333/iji.2022.1534a

Self-Regulated Learning in Kenyan Classrooms: A Test of ePEARL, a Process e-Portfolio

2022· article· en· W4283581579 on OpenAlex
L. L. Lysenko, C. Anne Wade, Philip C. Abrami, Rose Iminza, Enos Kiforo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Instruction · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsConcordia University
FundersInternational Development Research Centre
KeywordsKenyaMathematics educationPortfolioContext (archaeology)Test (biology)Process (computing)Educational technologyPsychologyAcademic achievementComputer sciencePedagogyPolitical scienceBusiness

Abstract

fetched live from OpenAlex

To align with Kenya 2030 Vision of education for self-reliance, there is a growing need for classroom instruction that develops students' capacity to be in control of their learning. This paper reports a two-year study that tested feasibility of implementing ePEARL, an e-portfolio, in the context of Kenyan public schools. By design, the digital portfolio supports the key learning processes though the phases of self-regulated learning --forethought, performance, and self-reflection. In this study, students (N=137) from four secondary classrooms used the tool as part of classroom instruction to complete their project assignments. Repeated measures analyses revealed that, over-time, students who demonstrated fuller use of ePEARL made significantly higher gains and reported higher level of selfregulated strategies compared to their classmates who hardly used the tool. The results suggest that in order to yield important benefits, the tool should be meaningfully integrated into classroom instruction.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.357
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it