Reflections on the implications of technology-mediated learning: A teacher educator perspective
Bibliographic record
Abstract
In this paper, I report on how my practice was influenced by re-designing an existing introductory teacher education course that included an online component. The purpose of the study was to explore the degree to which integrating technology affected my professional relationship with pre-service teachers and with sessional seminar leaders who taught the seminar portion of the course. The findings indicate positive benefits associated with the information technology. Two major themes characterizing my experiences are discussed: (1) relationships with students (seeking personal connections, engaging students in learning, and maximizing communication and expectations), and (2) relationships with seminar leaders (being an administrator and being a supporter). Overall, results indicate a number of issues: students embrace the technology as a way of learning, the electronic component of the course benefited face-to-face contact among students and instructors, and the online features of the course encouraged cooperation among students and instructors.
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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".