Combining Game-Based Learning with Design Thinking Using Block-based Programming to Enhance Computational Thinking and Creative Game for Primary Students
Bibliographic record
Abstract
This research aims to develop a combining game-based learning with design thinking using block-based programming to enhance computational thinking and creative games for primary students and will be referred to as game-based learning from now on. The purpose of this research is to 1) develop a model for game-based learning, 2) develop the system for game-based learning, 3) evaluate students' computational thinking after implementing game-based learning, and 4) evaluate the creative games created by students after game-based learning is implemented. The research tools included 1) the model for the game-based learning, 2) the model-appropriate evaluation form, 3) the learning system evaluation form 4) the computational thinking evaluation form, and 5) the creative game evaluation form. The research results showed that 1) the evaluation results of the model are appropriate for teaching at the highest level (x = 4.82, SD = 0.42) and could be used for experimental teaching, 2) The results of game-based learning system quality are at the highest level (x = 4. 57, S.D. = 0. 50). The researcher implements a game-based learning model and system to teach a sample group of 24 students in grade 4, using a purposive sampling method. The results of the implementation could be summarized as follows: 1) the results of students' computational thinking evaluation after implementing the model and system are significantly higher than before at the .05 level. 2) evaluation results of creative games that students developed after implementing the model and system are at a high level (x = 4.29, S.D. = 0.52)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".