Weaving a Personal Web: Using online technologies to create customized, connected, and dynamic learning environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores how personal web technologies (PWTs) can be used by learners and the relationship between PWTs and connectivist learning principles. Descriptions and applications of several technologies including social bookmarking tools, personal publishing platforms, and aggregators are also included. With these tools, individuals can create and manage personal learning environments (PLEs) and personal learning networks (PLNs), which have the potential to become powerful resources for academic, professional, and personal development. Résumé : Cet article explore les diverses façons dont les technologies Web personnelles peuvent être utilisées par les apprenants, ainsi que la relation entre ces technologies et les principes d’apprentissage connectivistes. Y sont également présentées les descriptions et les applications de plusieurs technologies, y compris les outils sociaux de mise en signet, les plateformes de publication personnelles et les agrégateurs. Ainsi outillées, les personnes peuvent créer et gérer des environnements d’apprentissage personnels (EAP) et des réseaux d’apprentissage personnels (RAP) qui recèlent le potentiel de devenir de puissantes ressources de perfectionnement sur les plans universitaire, professionnel et personnel.
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.
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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 it