Bibliographic record
Abstract
In recent years, different professional and academic settings have been increasingly utilizing ePortfolios to serve multiple purposes from recruitment to evaluation. This p aper analyzes ePortfolios created by graduate students at a Canadian university. Demonstrated is how students’ constructions can, and should, be more than a simple compilation of artifacts. Examined is an online learning environment whereby we shared knowledge, supported one another in knowledge construction, developed collective expertise, and engaged in progressive discourse. In our analysis of the portfolios, we focused on reflection and deepening understanding of learning. We discussed students’ use of metaphors and hypertexts as means of making cognitive connections. We found that when students understood technological tools and how to use them to substantiate their thinking processes and to engage the readers/ viewers, their ePortfolios were richer and more complex in their illustrations of learning. With more experience and further analysis of exemplars of existing portfolios, students became more nuanced in their organization of their ePortfolios, reflecting the messages they conveyed. Metaphors and hypertexts became useful vehicles to move away from linearity and chronology to new organizational modes that better illustrated students’ cognitive processes. In such a community of inquiry, developed within an online learning space, the instructor and peers had an important role in enhancing reflection through scaffolding. We conclude the paper with a call to explore the interactions between viewer/ reader and the materials presented in portfolios as part of learning occasions.
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.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".