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What Management Education can do About the Skills Gap: System Problem Versus Student Problem

2025· article· en· W4416000616 on OpenAlexaff
Sonja Johnston, Michele Jacobsen, Douglas B. Clark, Sharon Friesen

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsUniversity of CalgarySAIT Polytechnic
Fundersnot available
KeywordsCapstoneCurriculumSkills managementSystems thinkingExperiential learningConceptual frameworkParallelsHigher educationGrounded theory

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.417
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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