Exploring cross-disciplinary differences in course mode, instructional tools and teaching methods in online courses in business management
Bibliographic record
Abstract
Building on research from past decades, this paper explores cross-disciplinary curricular and teaching differences in online, blended and web-facilitated business and management courses. Based on an online survey of 240 USA, Canadian and European university instructors, the authors examine if faculty differ in their preferred course mode (the degree of online delivery), instructional tools used, and teaching methods, by discipline types (hard or soft) and five subject groups. The research found cross-disciplinary differences in the use of some of the 29 instructional tools surveyed (e.g. online group projects, group tools like wikis, and specialized software) and in teaching methods (didactic, dialectic dialogic, dialectic collaborative and heuristic). No significant disciplinary differences were found in the instructor's choice of course mode perhaps pointing to wider engagement in online learning in all business and management disciplines.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".