From ePortfolios to iPortfolios: The find, refine, design, and bind model
Bibliographic record
Abstract
During the past two decades, educational institutions around the world began formalizing the process of collecting student work as a means of showcasing student accomplishments and ultimately providing students a forum for reflecting on their accomplishments. In this article, the authors propose a redefinition of the electronic portfolio development process in a way that supports 21st century learning in which students FIND, REFINE, DESIGN, and BIND materials in the virtual "cloud”. The authors believe that by incorporating Web 2.0 tools educators will have the opportunity to technologically integrate the students’ school work into their general work flow, their entertainment flow, and ultimately into their web-based curricular work flow. Integration will hopefully reduce student resistance to portfolio development as an add-on activity and allow portfolios to become more personal, providing a richer connection between the students’ lives, their education, and their paths to knowledge. Résumé : Au cours des deux dernières décennies, les établissements d’enseignement à travers le monde ont commencé à formaliser le processus de collecte des travaux des élèves comme moyen de présenter leurs réalisations et, à terme, de fournir aux élèves un forum leur permettant de réfléchir sur leurs réalisations. Dans cet article, les auteurs proposent de redéfinir le processus d’élaboration du portfolio électronique de manière à ce qu’il soutienne l’apprentissage du 21e siècle au sein duquel les élèves TROUVENT et AFFINENT les matériaux, CONÇOIVENT une présentation et LIENT les éléments ensemble dans le « nuage » virtuel. Les auteurs estiment qu’incorporer les outils Web 2.0 donnera aux éducateurs l’occasion d’intégrer technologiquement les travaux scolaires des élèves dans leur flux de travaux général, leur flux de divertissement et, enfin, dans leur flux de travaux scolaires en ligne. Cette intégration, nous l’espérons, permettra de diminuer la réticence des étudiants à l’égard des portfolios en présentant leur élaboration comme une activité complémentaire; les portfolios pourraient ainsi devenir plus personnels et constituer un lien plus riche entre la vie des élèves, leur éducation, et leurs chemins vers la connaissance.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".