Collaborative engagement experience-based learning: a teaching framework for business education
Bibliographic record
Abstract
Purpose An academic–practitioner divide exists suggesting the need for business education curriculum to more appropriately suit private-sector demands. This calls for pedagogical approaches that offer experiences and build skill sets to better prepare graduates for the workforce. The authors propose a framework, collaborative engagement experience-based learning (CEEBL), as a new pedagogical method for teaching and learning in business education. This research provides a viable solution to bridge the gap between academia and industry. The authors suggest CEEBL also offers business students new methods of engagement in the world of work. Design/methodology/approach This exploratory study investigates the CEEBL framework applied to a business education course in competitive intelligence (CI) and a crisis simulation exercise that offer “real world” experiences to students. Data were collected in two semesters and included feedback from over 70 undergraduate students. Findings Results suggest that the CEEBL framework provides students with the learning experiences to build much-needed skill sets. Additionally, Hallinger and Lu's (2011) assessment of overall instructional effectiveness showed positive statistical results for its dimensions. Originality/value The CEEBL framework is coined from two existing pedagogical underpinnings; collaborative engagement (CE) and experience-based Learning (EBL). These concepts offer insights into the ways in which CE promotes a rich learning experience. The new framework takes into consideration the relationship(s) among the dimensions of CE and EBL and how they intertwine with each other to create a pedagogical method that can better prepare students for a dynamic workplace. CEEBL can be easily adapted for online, hybrid or in-session teaching environments. Additionally, the framework offers flexibility in application to other disciplines while addressing current topics and issues through the capstone exercise.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".