Deconstructing the Notion of ePortfolio as a ‘High Impact Practice’: A Self-Study and Comparative Analysis
Bibliographic record
Abstract
ePortfolio has become a popular pedagogical tool on the higher educational landscape, often referred to as a “high impact practice” that has the potential to generate transformative learning experiences. After reflecting on our educational development consultations and undergraduate teaching practices with ePortfolio, we identified areas of resonance with, and misalignment between, research literature and our experiences with implementation. We have conducted a self-study to capture the narratives of our experiences, and engaged in a comparative analysis of these narratives alongside ePortfolio best practice literature. We provide a comprehensive literature review, an overview of our narratives, and a discussion about the inconsistencies arising from our comparison. We conclude by offering some recommendations for application and suggestions for further inquiry. L’ePortfolio est devenu un outil pédagogique populaire sur la scène de l’enseignement supérieur, on en parle souvent comme d’une « pratique à fort impact » qui a le potentiel de générer des expériences d’apprentissage transformateur. Après avoir examiné nos consultations en matière de développement éducationnel et de pratiques d’enseignement au niveau du premier cycle avec emploi d’un ePortfolio, nous avons identifié des zones de résonnance ainsi que des dissonances par rapport à la recherche publiée et à nos expériences de mise en oeuvre. Nous avons mené une auto-évaluation afin de saisir les descriptions de nos expériences ainsi qu’une analyse comparative de ces descriptions côte à côte avec la documentation publiée sur les meilleures pratiques en matière d’ePortfolio. Nous présentons un examen complet de la documentation publiée, une vue d’ensemble de nos descriptions et une discussion sur les contradictions qui découlent de notre comparaison. En conclusion, nous offrons quelques recommandations concernant la mise en application ainsi que des suggestions pour un complément d’examen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".