What Makes an Effective Virtual Learning Experience for Promoting Faculty Use of Technology
Bibliographic record
Abstract
The main objective of this project was to provide a Web-based tool that included practical resources for faculty in education at one Canadian university who were seeking ways to make effective use of learning technologies to enhance learning environments. The site includes online tools and resources and integrates ideas from people in the faculty and other education institutions about various ways to enhance teaching and learning through the use of technology. A study using the think-aloud strategy investigated the views of four faculty members about the effectiveness of the site for their own professional development. L’objectif principal de ce projet consistait à offrir un outil sur Web qui incluait des ressources pratiques pour la faculté en éducation d’une université canadienne qui recherchait les façons de faire une utilisation efficace de technologies d’apprentissage pour améliorer l’environnement d’apprentissage. Le site comprend des outils et ressources en ligne et intègre des idées de personnes de la faculté et d’autres institutions d’éducation concernant diverses manières d’améliorer l’enseignement et l’apprentissage par l’utilisation de la technologie. Une étude utilisant une stratégie d’échange d’idées a interrogé quatre membres de la faculté concernant l’efficacité du site pour leur propre développement professionnel.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".