Directions for research and development on electronic portfolios. Canadian Journal of Learning and
Bibliographic record
Abstract
Abstract: This lead article for the special issue of the Canadian Journal of Learning and Technology explores directions for research and development on electronic portfolios, which are digital containers capable of storing visual and auditory content; software for which may also be designed to support a variety of pedagogical processes and assessment purposes. The paper is organized around several key questions: What are the types and characteristics of electronic portfolios? What are the outcomes and processes that electronic portfolios support for their creators? What are the contexts in which EPs are most effective and worthwhile? Who are electronic portfolio users/viewers and how do we provide appropriate professional development to encourage correct adoption and widespread and sustained use? What do we know and need to know about technical and administrative issues? What is evidence of electronic portfolio success? How do we move forward with funding and infrastructure?
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".