A COMMUNITY OF PRACTICES FOR ACCELERATING THE ADOPTION OF INFORMATION TECHNOLOGY IN ENGINEERING EDUCATION
Bibliographic record
Abstract
Abstract ⎯ There are serious problems in the adoption of information technology (IT) for teaching in engineering. Professors hesitate to use IT for teaching: they are not familiar with the technologies, and know little about the theories and practices around their use. The research is part of an initiative undertaken by a group of early IT adopters to build and share new knowledge related with the use of technology for teaching. The paper presents an innovative approach to accelerate the adoption of IT for teaching and improve its value for transferring knowledge. Three types of technologies are analyzed: intelligent boards, audience response systems and community-based tools for learning. Practices from superusers are captured using ethnographic methods. Members of the community validate these practices through experimentation in a learning laboratory. Then a framework of practices is developed and shared within the community’s knowledge base. Index Terms ⎯ IT in teaching, community of practice, best practices, knowledge portal.
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 | 0.034 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".