Pedagogical innovation in strategic management education : A case study of course redesign for virtual-hybrid delivery
Bibliographic record
Abstract
This paper examines the pedagogical redesign of a senior-level Strategic Management course within an engineering management programme. In response to the COVID-19 pandemic, the course transitioned from a traditional face-to-face (F2F) format to a virtual-hybrid delivery model. The course, which emphasises practical business strategy, required a comprehensive redesign to maintain its interactive and immersive nature in an online environment. The new virtual-hybrid approach enhanced student engagement, flexibility and learning outcomes by integrating synchronous and asynchronous learning approaches. The course design was guided by three key criteria: (1) rethinking the traditional three-hour weekly course structure to focus on competency development rather than adherence to the three-credit-hour model; (2) limiting large-scale virtual lectures to the initial weeks, followed by bi-weekly small-group student meetings with instructors; and (3) creating high-quality instructional videos to support asynchronous learning. These innovations allowed students to engage more deeply with course content at their own pace while maintaining personalised interactions with instructors through regular meetings. Student feedback from surveys and university course evaluations indicated strong satisfaction with the redesigned course. Key strengths included the course’s clear structure, the flexibility provided by instructional videos and the personalised feedback offered during team meetings. A notable outcome was a 13 per cent improvement in average final grades compared to previous F2F offerings. This case study provides valuable insights into the effective integration of synchronous and asynchronous learning tools, offering recommendations for educators seeking to adapt their pedagogical approaches in the evolving landscape of higher education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".