Gamified Learning: Teaching Coding and Creative Thinking with Minecraft: Education Edition (M:EE) for Thai Students
Bibliographic record
Abstract
This study explores the challenges and effectiveness of game-based learning (GBL) through Minecraft: Education Edition (M:EE) in teaching coding and fostering creative thinking among fourth-grade students in Thailand. Conducted over eight weeks in 2023 at a laboratory school in Bangkok, the study involved 284 students. Data was gathered using scoring rubrics and reflection questionnaires to assess the impact on students’ coding and creative thinking skills. The results indicated that students reached high levels of performance in both areas, although more complex coding tasks posed challenges. Notably, performance was consistent across genders, but students who struggled with navigating the platform tended to score lower. Several challenges were identified, including limited English proficiency, platform instability, and difficulties in mouse and keyboard usage. Despite these challenges, students expressed high levels of enjoyment, particularly in collaborating on creative projects. They also demonstrated improvements in teamwork, problem-solving, and communication skills. The immersive and interactive nature of M:EE was well-received, fostering engagement and collaboration among the students. While the overall results suggest that M:EE is an effective tool for enhancing coding and creative thinking skills in young learners, the study also highlights areas for improvement. Technical difficulties, such as platform instability, and the need for additional support for students struggling with the platform, were noted as areas requiring attention. Furthermore, the study suggests that future research could explore ways to integrate additional scaffolding and support to address the challenges encountered by students with limited English proficiency and those less familiar with digital tools. Overall, the study affirms the potential of M:EE as a valuable tool in GBL for young learners, with further refinement of pedagogical approaches recommended to enhance its effectiveness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".