What Management Education can do About the Skills Gap: System Problem Versus Student Problem
Bibliographic record
Abstract
In this paper, we reconceptualize the skills gap in management education between graduate competency and employer expectations as a systems problem rather than a student deficit. We challenge the view that the skills gap can be addressed by merely adding more content or targeted skills to the curriculum in response to employer demands. From a logical analysis of a business capstone course, we draw parallels between theoretical perspectives in management education, educational research, and the learning sciences to create a conceptual framework for optimizing learner success. Our analysis and conceptual framework have practical implications for the redesign of capstone. We take a systems thinking approach, grounded in educational research theory and practice, to address the skills gap. Our findings offer both theoretical and practical considerations in this case study of practice. Four interconnected experience dimensions are identified that are essential for student success: (1) coherence, (2) integrated learning, (3) meaningful relevance, and (4) future-ready orientation. Viewed through a systems thinking lens, the four dimensions provide actionable insights for leaders, educators, and decision-makers in management education. We present a reconceptualization of the skills gap that advocates for purposeful and systemic learning design in management education and beyond.
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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".