Employing the EPEC Hierarchy of Conditions (Version II) To Evaluate the Effectiveness of Using Synchronous Technologies with Multi-Location Student Cohorts in the Tertiary Education Setting
Bibliographic record
Abstract
As e-learning maintains its popularity worldwide, and university enrolments continue to rise, online tertiary level coursework is increasingly being designed for groups of distributed learners, as opposed to individual students. Many institutions struggle with incorporating all facets of online learning and teaching capabilities with the range and variety of software tools available to them. This study used the EPEC Hierarchy of Conditions (ease of use, psychologically safe environment, e-learning self-efficacy, and competence) for E-Learning/E-Teaching Competence (Version II) to investigate the effectiveness of an online synchronous platform to train pre-service teachers studying in groups at multiple distance locations called satellite campuses. The study included 58 pre-service teachers: 14 who were online using individual computers and 44 joining online, sitting physically together in groups, at various locations. Students completed a survey at the conclusion of the coursework and data were analyzed using a mixed methods approach. This study’s findings support the EPEC model applied in this context, which holds that success with e-learning and e-teaching is dependent on four preconditions: 1) ease of use, 2) psychologically safe environment, 3) e-learning self-efficacy, and 4) competency. However, the results also suggest two other factors that impact the success of the online learning experience when working with various sized groups. The study demonstrates that the effectiveness of a multi-location group model may not be dependent only on the EPEC preconditions but also the effectiveness of the instructor support present and the appropriateness of the tool being implemented. This has led to the revised EPEC Hierarchy of Conditions for E-Learning/E-Teaching Competence (Version III).
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".