Impact of Adaptive Educational Game Applications on Improving Student Learning: Efforts to Introduce Nusantara Culture in Indonesia
Bibliographic record
Abstract
This study aimed to introduce Nusantara culture based on educational games by adjusting students' learning styles. Culture as an ancestral heritage tradition needs to be preserved by introducing it to the younger gen-eration from an early age. However, the survey results found that less than 26% of student respondents un-derstood Nusantara culture well. Compared to previous research, the model of cultural introduction through adaptive educational games is more fun because it is adapted to the way students learn. This research was carried out using the Design-Based Research (DBR) method through 4 stages of the procedure. The feasibil-ity test and application effectiveness test were carried out on a group of students from several elementary schools in Indonesia, who were taken using a cluster random sampling technique. The results of media design and content validation obtained an average value of 0.76 and 0.82, which means that the media is declared valid. The feasibility test used the System Usability Scale (SUS) with an average value of 80% in the acceptable category. The results of the research obtained a description of the comparison of the final scores of the control class and the experimental class, which was 55 compared to 75. This study concluded that learning media for introducing Indonesian culture based on adaptive educational games had a positive impact by effectively increasing learning outcomes on students' understanding of Indonesian culture. Further development of this game application can be expanded in the application of animation in more depth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".