Comparing face-to-face courses and online courses with ‘enhanced strategic’ teacher video presence
Bibliographic record
Abstract
The majority of research comparing face-to-face (F2F) and online education finds no significant differences in student satisfaction or learning outcomes. However, in view of the evolving nature of online education, the topic still warrants investigation. This study examines whether student satisfaction and performance differ between F2F courses and asynchronous online courses with an ‘enhanced strategic’ teacher video presence. These online courses do not directly mimic F2F courses but use strategic teacher video appearances with the aim of reducing students’ extraneous cognitive load while maintaining some teacher presence. Data were obtained using surveys and student course assessments in a required undergraduate course in advanced financial accounting. Focus groups were conducted ex-post to gain a deeper understanding of the PLS-SEM models’ results. The findings show no significant differences in student satisfaction or performance between F2F courses and online courses with ‘enhanced strategic’ teacher video presence. These findings suggest online education can be as effective as F2F, but also underline the need to uncover methods through which online teaching can improve upon traditional F2F if research is to find any difference between the two delivery modes.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".