Game-based student e-learning experience: Empirical evidence from private universities in Jordan
Bibliographic record
Abstract
This study investigates the impact of game-based (gamification) e-Learning techniques on students' engagement, thereby, their satisfaction with e-learning in Jordanian private universities. A conceptual model was developed based on existing empirical evidence from the literature. Data was then collected through a self-administered questionnaire survey from 198 private university students, who were conveniently selected for the study. The data was analyzed using Structural Equation Modeling (SEM) with smart PLS 23. Data analysis revealed a positive effect of gamification on both student engagement and satisfaction, suggesting that incorporating gaming elements into the e-learning process in Jordanian private universities led to higher levels of student engagement, thereby greater student satisfaction with the e-learning experience. A mediating role was also found for student engagement on the effect of gamification on student satisfaction. The findings provide insights to practitioners on how gamification can be utilized as an effective strategy to deliver a more enjoyable and interactive e-learning experience. Research findings were discussed, and conclusions and implications were lastly provided.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".