Bridging Engagement and Learning Outcomes
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,089 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,001 | 0,014 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».