Pre-Service Perspectives on E-Teaching: Assessing E-Teaching Using the EPEC Hierarchy of Conditions for E-Learning/Teaching Competence
Bibliographic record
Abstract
This article examines pre-service teacher perspectives of teaching with an online synchronous (live-time) platform as a part of their training. Fifty-three students who participated in a blended learning (including both face-to-face and online lectures) course were assessed in a teaching simulation through an online presentation, and participated in questionnaires and interviews about their experiences as e-learners using the platform. The EPEC hierarchy of conditions (Ease of use, Psychologically safe environment, e-learning/e-teaching Efficacy, and e-learning Competence) for e-learning competency, developed based on an analysis of pre-service teachers’ experience as e-learners in this same study, was used as a framework to assess teacher perspectives as e-teachers using this technology. Qualitative interview data were collected about students’ experiences using the platform, and analyzed via thematic content analysis. The findings showed that students generally favoured the online e-teaching synchronous platform over in-person presentations, and the quality of online presentations was considered at least as good as in person.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".