Bridging Engagement and Learning Outcomes
Bibliographic record
Abstract
Enterprise Resource Planning (ERP) systems are central to modern organizational operations, yet effectively teaching these complex systems to students remains a significant pedagogical challenge in higher education (Wijaya, 2023). ERPsim, a simulation-based learning tool built on SAP ERP, is now widely implemented to provide students with immersive, experiential learning in realistic business environments (HEC Montréal., 2025). While ERPsim has been extensively studied and shown to improve student comprehension of ERP concepts, gaps remain in understanding its effectiveness in achieving specific learning outcomes within information systems (IS) subjects and how it can be strategically integrated into broader curricula to balance hands-on experience with critical theoretical understanding (Faisal et al., 2022). To address these gaps, we integrated ERPsim into tutorials of a master’s-level IS subject at a leading Australian university. We assessed its effectiveness against three defined learning outcomes, with the aim of systematizing its integration into the curriculum. The study examined ERPsim in an IS subject with three critical learning outcomes: (1) understand the benefits that ERP systems provide to organizations, (2) explain the mechanisms through which ERP systems deliver these benefits, and (3) develop practical skills in operating ERP systems. ERPsim’s logistics sustainability game was deployed across three tutorial sessions, each with 20-minute rounds (10 virtual days, 2 mins/day), requiring students to work in teams and make real-time operational decisions in response to market dynamics. The simulation increased in complexity each week. Debriefing was conducted after gameplay to help students reflect on how their hands-on experience demonstrated ERP benefits and mechanisms. A mixed-method approach was used to evaluate the effectiveness of ERPsim, guided by a conceptual framework incorporating gamification, self-determination theory, and situated learning theory (Alserri et al., 2019; Neys et al., 2014; Goel et al., 2010). Our subject had a total of 67 students. Pre- and post-game surveys, completed by 32 and 36 students respectively, measured changes in understanding of ERP concepts and confidence in operating SAP ERP. Additionally, enjoyment, engagement, and perceived authenticity were assessed. The survey results were analysed using the Mann–Whitney U test (Field, 2017). The teaching team also conducted reflective evaluations to assess the learning experience. The survey results indicated that students showed improved understanding of ERP benefits and mechanisms with Mann–Whitney U values ranging from 293 to 390 (p < 0.05, r = 0.2771 to 0.4217), and gained confidence operating SAP ERP (U = 399, p < 0.05, r = 0.2637). While ERPsim supported students in better achieving the learning outcomes, the study found that it was best suited as an introductory learning tool, as its heavy automation often masked the intricate details of ERP systems. Additionally, the fast-paced gameplay limited opportunities for students to engage in deeper conceptual exploration. Our findings suggest that while ERPsim effectively contextualized IS concepts to support the achievement of learning outcomes, it was insufficient for achieving them comprehensively. This aligns with the conclusions of Wang et al. (2024). Theoretically, this highlights the need to consider ERPsim not as a standalone solution but as a supportive tool within a broader pedagogical framework that fosters deep learning. Practically, it emphasizes the need for educators to design a holistic teaching strategy around ERPsim to maximize its educational impact.
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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 | 0.016 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".