Creating Meaningful Learning and Improving Students’ Knowledge Using Game-based Learning in Higher Education
Bibliographic record
Abstract
This paper aimed at investigating the use of game-based learning approach in creating meaningful learning experiences and its influence in improving students’ knowledge on the subject matter. A total number of 120 students enrolled in the accounting system analysis and design course in a university participated in this experimental study. Grounded by Kolb’s experiential learning theory, an experiment was conducted to explore how participants involve themselves in learning from experience by playing educational game. Data were collected through pre and post quiz scores, questionnaire surveys, interviews, and reflections on the students’ feedback. The results of the t-test analysis showed that students achieved higher marks after the game intervention and the difference in the mean score between before and after the game played is statistically significant. The findings suggested that the game-based learning approach in teaching and learning can assist students in understanding the subject better than the teacher-centred approach. The results of the study also found that the learning sessions were more engaging and fun as the respondents enjoyed learning through playing games. In addition, the game-based learning approach motivates them to conduct self-study while seeking answers to solve the game tasks. Practically, this study contributes to disseminate awareness among academicians and industry practitioners in developing more educational games that can be used in teaching and learning. Theoretically, it contributes to the existing literature of game-based teaching approach.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".