Simulation Games for Active Learning of ERP Concepts.
Bibliographic record
Abstract
Teaching the concepts underlying an Enterprise Resource Planning (ERP) system is a difficult task. Many students have very little IT experience to which they can relate these concepts. They may have acquired business experience in one or two functional areas, but many of them have only a limited understanding of the operational aspects supporting the value creation process in modern firms. Moreover, they usually have had no firsthand experience with the functional non-integrated software that the ERP system was designed to replace. For these students, the horizontal integration of the firm can be very abstract due to their lack of hands-on experience with legacy systems. \\ \\ ERPsim solutions are innovative ââ¬Ålearning-by-doingââ¬Â approaches to teaching ERP concepts. Several administrative functions in SAP are automated, so that students can focus on making business decisions. Using a mix of the ERP systemââ¬â¢s standard transactions and customized reports, students must analyze information and make business decisions to ensure the profitability of their operations. The main learning objectives of this game are: (i) to develop a hands-on understanding of the concepts underlying enterprise systems, (ii) to experience the benefits of enterprise integration, (iii) and to develop technical skills using ERP software. \\ \\ The ERPsim Lab develops innovative teaching solutions by transforming business software into dynamic learning platforms. In this workshop, the labââ¬â¢s flagship solution, ERPsim, will be introduced. This solution enables participants to understand ERP concepts by experiencing a continuous-time simulation where they have to run a business by leveraging the capabilities of SAP ERP and S/4HANA. Through the experience, the link between ERPsim and SAP will be demonstrated. \\ \\ The participants will also become familiar with other activities of the ERPsim Lab such as solutions that are developed for teaching data analytics concepts. \\
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".