Experiential Learning Spaces for Enterprise Resource Planning Courses in Business Schools
Bibliographic record
Abstract
Enterprise Resource Planning (ERP) systems have experienced mainstream adoption as a comprehensive solution for business function integration and end-to-end process management. To meet industry demand, many business schools offer academic courses in ERP strategy and technology to train students to be proficient in the use of ERP systems. The extant literature on ERP education highlights the need for additional research about pedagogical techniques that can help business schools in their efforts. To be effective in the delivery of ERP courses, instructors require a deeper understanding of how they can teach ERP systems in a meaningful way and consequently foster higher levels of student engagement. This paper provides an overview of the current state of ERP academic programs and teaching practices. This is followed by a review of experiential learning theory (ELT) which is later used in this paper as a basis for pedagogical practice suggestions towards the improvement of ERP training. A pedagogical model comprising of various course activities and teaching practices is proposed. The proposed practices are justified in terms of their efficacy towards developing experiential learning spaces for students to cultivate theoretical and applied knowledge of ERP systems.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".