What Matters for MBA Teaching Quality A Systematic Literature Review of Knowledge Delivery and Teaching Methods
Bibliographic record
Abstract
MBA programs are fundamental to business schools, but recent declines in enrollment and the diminishing value of MBA qualifications, exacerbated by global economic uncertainty, have prompted schools to seek strategies for recovery. To address these challenges, it is crucial to enhance the content, delivery methods, and teaching approaches. Numerous studies highlight the importance of pedagogical content in optimizing MBA programs, as the knowledge conveyed is meant not only to be theoretical but also to guide students in their management practice. Our research demonstrates that combining case-based teaching from experiential learning with simulation-based methods from active learning significantly improves knowledge transfer. Through a systematic literature review, we developed a framework that aligns teaching methods with the content being taught. The findings suggest that accurately characterizing the curriculum content and balancing teaching methods based on different pedagogical approaches can enhance the effectiveness of MBA instruction. These strategies offer potential solutions to counter the current challenges faced by MBA programs and improve their overall impact. By adopting such methods, business schools can better meet the evolving needs of students and the demands of the global business environment.4o
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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 | 0.077 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".