Exploiter son portfolio numérique : construire son identité professionnelle numérique pour valoriser ses competences / The use of a personal digital portfolio: how to build its own professional digital identity and enhance its competences
Bibliographic record
Abstract
Résumé : Les étudiants de l’enseignement supérieur et des formations professionnelles sont confrontés aux exigences d’employabilité et de mobilité professionnelle. Comment s’y préparer ? Quelles compétences développer ? Quel dispositif pédagogique peut répondre, durablement, à ces questions ? Cet article pose un regard de synthèse sur les résultats de six études relatives aux usages du portfolio numérique utilisé dans une perspective de construction d’une identité professionnelle numérique. L’article propose une reconception d’un dispositif d’accompagnement des étudiants autour de deux axes majeurs : le développement d’une compétence à “l’auto reconnaissance de ses propres compétences”, d’une part, et le développement d’une compétence à « l’auto socialisation », d’autre part. Abstract: Students in higher education and vocational training have to face high demands of employability and professional mobility. How can they get ready? What competencies should they develop? What kind of pedagogical device can provide robust answers to these questions? This article summarizes the results of six studies on the uses of digital portfolios to build a professional digital identity. In this article, we suggest redesigning support for students in two major ways: first, moving up from competency development to “self-acknowledgement of one’s own competencies”, and second, from competency development to “self-socialization”.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".