Exploring amplifications and reductions associated with e‐learning: conversations with leaders of e‐learning programs
Bibliographic record
Abstract
The purpose of this study was to probe more deeply into the changes that are occurring in higher education as a result of the use of e‐learning technology. An interpretive approach using unstructured interviews with leaders of e‐learning programs at research‐intensive universities was conducted. Based on the findings of this study, we conclude that (1) competing paradigms which suggest that there are associated amplifications and reductions occurring as a result of e‐learning technologies can contribute to a renewed discussion on the use of e‐learning in higher education and (2) the perception that e‐learning technologies are pedagogically neutral is misguided. The results of this study indicate we should be aware that we are operating within the technology’s structure and that there are unavoidable consequences. How much these consequences matter to us depends on whether they are compatible with our pedagogical aims and objectives.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".