Authentic OM problem solving in an ERP context
Bibliographic record
Abstract
Purpose It is argued that problem‐based learning (PBL) is a valuable approach to teaching operations management, as it allows learners to apply their knowledge and skills in an environment that is close to real‐life. In fact, many simulations currently exist in the teaching of operations management. However, these simulations lack a connection to real‐life, as they are typically turn‐based and do not use real‐life IT support. The current paper seeks to address this issue by presenting an innovative pedagogical approach designed to provide learners with an authentic problem‐solving experience in operations management within an enterprise resource planning (ERP) system. Design/methodology/approach The paper proposes a simulation game called ERPsim whereby students must operate an enterprise in a simulated economic environment using in real time a real‐life ERP system, namely SAP. Based on a survey with instructors, it assesses the extent to which this proposed simulation is aligned with the five characteristics of the PBL approach. Findings Survey respondents confirm that significant improvements in student evaluations, learner motivation, attendance, and engagement, as well as increased learner competence with the technology can be achieved by using the proposed approach. Practical implications For more than five years this pedagogical approach has been used by more than 250 professors, lecturers, and professional trainers in over 160 universities worldwide. Between September 2009 and June 2011, more than 3,000 simulations games were played by over 16,000 university student teams. Originality/value Results and observations on using the proposed pedagogical approach are presented and compared to the main characteristics of the PBL approach (authenticity, ill structured problems, student‐centered, small group settings and facilitator dimensions).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".