Enhancing Digital Creativity in Higher Education Through Gamified Distance Learning Systems
Bibliographic record
Abstract
The study aimed to examine the impact of a gamified distance learning system on enhancing digital creativity in distance education. The research involved 36 graduate students from Sukhothai Thammathirat Open University enrolled in the Public Administration Innovation course. Over a six-week experimental period, the learning system incorporated gamification elements, including missions, challenges, and collaborative tasks, to promote engagement and skill development. Data collection tools comprised 1) a digital creativity assessment form, 2) an evaluation rubric for learners’ digital innovation projects, and 3) a satisfaction survey regarding the gamified learning system. The data were analyzed using t-test dependent statistics to compare digital creativity scores before and after the intervention, rubric criteria for project quality evaluation, and descriptive statistics for satisfaction levels. Results showed a significant increase in digital creativity, with post-test scores (M = 4.49, SD = 0.21) significantly higher than pre-test scores (M = 3.19, SD = 0.19) at the 0.05 significance level. Rubric-based evaluations indicated that learners’ digital innovation projects were rated between good and very good. Additionally, learners expressed high satisfaction with the gamified system, with an average score of 4.35 (SD = 0.69). This study provides evidence of the effectiveness of gamification in fostering digital creativity, addressing a notable gap in traditional educational methods. The integration of game-based elements within distance education supports increased interaction, motivation, and collaboration among learners. The findings underscore the potential of gamified learning systems as a strategic tool for enhancing essential digital skills and creativity in higher education. The study highlights practical implications for curriculum design and offers innovative approaches to address the challenges of distance learning in the digital age. Further exploration is recommended to optimize gamification designs for diverse educational contexts and learner needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".