Can Gamification Change Learners’ Ability and Motivation? Role of Eustress in the Context of Gamified ERP Training
Bibliographic record
Abstract
Gamification, using game elements in nongame contexts, is widely recognized for its efficacy in training individuals within organizational and academic environments.Specifically, it has gained significant attention in training to operate complex systems like enterprise resource planning (ERP).However, research has generally overlooked how gamified training changes users' ability and motivation through eustress (challenging and positive stress).To address this gap, this study proposes that gamified training can increase users' ability and motivation.Self-efficacy and involvement are examined as representations of ability and motivation, respectively.Furthermore, we hypothesize that users' pre-training self-efficacy and involvement positively affect eustress experienced during gamified training, subsequently affecting post-training self-efficacy and involvement.To test research hypotheses, data were collected from 205 graduate students who participated in an ERP simulation game.The findings indicate a significant increase in self-efficacy and involvement after gamified training, substantiating the potential of gamification to enhance these aspects.Moreover, the study demonstrates that initial self-efficacy in learning ERP systems influences the experience of eustress during gamified training, ultimately impacting post-training self-efficacy and involvement.Our research sheds light on the transformative impact of gamified training and eustress in learning complex systems, providing valuable insights for gamification implementation in training practices.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".