Computer Game Development for Balancing Chemical Equations Skill in Chemistry Education
Bibliographic record
Abstract
This computer game was designed and developed to enhance the skills of high school students in balancing chemical equations. The game simulates a trip to the beach and consists of three missions, ranging from easy to difficult levels, enabling players to engage in a contextual learning experience. The objectives of this computer game development were to compare learning achievement before and after using the game and to evaluate the level of satisfaction in learning through the game. The study sample consisted of 27 high school students in Mathayom Suksa four, selected from a medium-sized school in Pathumthani Province, Thailand, using purposive sampling. The research employed two primary assessment tools: 1) a pretest-posttest to assess the understanding of balancing chemical equations and 2) a satisfaction evaluation form to evaluate the computer game. Data analysis was performed using the mean, standard deviation (S.D.), t-test, and normalized gain ( ). The findings revealed that the mean learning achievement after using the computer game (13.52) was significantly higher than the mean before using the computer game (11.52) at a significance level of 0.01. The overall normalized gain for the class was at a moderate level. Furthermore, the majority of the participants expressed high satisfaction with a mean score of 4.56, indicating that the computer game was user-friendly and conducive to learning. The content, which presented chemical reactions in daily life within the computer game, was also easily comprehensible. Therefore, it could be effectively utilized to enhance classroom learning and applied to other areas of knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".