What Is a Digital Electronic Portfolio in Teacher Education? A Case Study of Instructors’ and Students’ Enabling Insights on the Electronic Portfolio Process | Qu'est-ce qu'un portfolio numérique dans la formation des enseignants? Étude de cas sur les perspectives d’enseignants et d’étudiants concernant le processus du portfolio numérique.
Bibliographic record
Abstract
Professional programs in postsecondary education have long been using electronic portfolios (ePs) for diverse purposes, for example, assessment, certification, showcasing, and learning. However, in our practices of using ePs in teacher education for the past several years, we have found that the question of “what is an eP?” requires substantial unpacking. This paper will offer insights on our evolving understanding on ePs in teacher education based on three interacting areas: (a) rich media reframing how we understand professional learning in a digitally linked world; (b) literature from the last 10 years in the use of ePs; and (c) insights from instructors and students using an eP process in one term of a teacher education program. We conclude with a re-visioning of learning in teacher education in relation to the emerging practices enabled by an eP process.Les programmes professionnels d’éducation supérieure utilisent depuis longtemps des portfolios numériques à des fins diverses, comme l’évaluation, la certification, la mise en valeur et l’apprentissage. Cependant, notre expérience des dernières années relative à l’utilisation des portfolios numériques dans la formation des enseignants révèle que la définition du portfolio numérique nécessite un examen approfondi. Le présent article offre un aperçu de notre conception, toujours en évolution, des portfolios numériques dans la formation des enseignants, basée sur trois domaines en interaction: (a) des contenus médiatiques riches qui restructurent la manière dont nous comprenons l’apprentissage professionnel dans un monde interconnecté par le numérique; (b) la littérature des dix dernières années sur l'utilisation des portfolios numériques; et (c) les perspectives d’instructeurs et d’étudiants ayant utilisé des portfolios numériques durant un trimestre d'un programme de formation des enseignants. En conclusion, nous réimaginons l'apprentissage dans la formation des enseignants en tenant compte des pratiques émergentes rendues possibles par les portfolios numériques.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".