Bibliographic record
Abstract
As digital pedagogy and instructional strategy, electronic portfolios (ePortfolios) help educators organize instruction, facilitate teaching, and enhance learning. When students develop their ePortfolio projects in online spaces, they build a community where they learn to overcome challenges with the technology and to embrace the pedagogy that promotes learning. Decades-old research shows that the ePortfolio development process enhances knowledge production, makes visible knowledge application, and capacitates knowledge mobilization. ePortfolio technology promotes interaction, fosters reflection, and encourages both analytical thinking and the questioning of assumptions related to learning online. As multipurpose tools (assessment, accountability, collaboration, curriculum), ePortfolios are part of a movement that aims to reimagine the way we teach and learn in internet spaces. ePortfolio pedagogy, undergirded by interaction and reflection, integrates authentic learning episodes in digital spaces and enables practitioners to engage in democratizing and mobilizing knowledge. ePortfolio pedagogy is inclusive, embraces equity, and encourages the sharing of stories, beliefs, and ideas that result in appreciation of self and others. As students engage in idea generation in terms of choice of platform, layout, content, and artefacts, they experience a shift in mindset that capacitates a can-do attitude toward learning potential and project completion in online spaces.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".