The Development and Effectiveness of Game-Based Learning Prototypes for Daily Life Words at B1 Level: A Case Study of Engineering Students in Thailand
Bibliographic record
Abstract
In order to communicate effectively, it is undeniable that language learners should have rich vocabularies. In this study, online games are proposed to be an effective learning method in language teaching and learning. The study aimed at constructing game prototypes–online intermediate daily life lexicon (IDLL) games, assessing quality of online IDLL games, and investigating effectiveness of these games. This study comprised two stages: design and evaluation. Quality of the online games was validated by four experts and 109 games players. Experts were asked to complete the IDLL evaluation form, whereas 109 game players were assigned to complete the learning experience questionnaire and learners’ satisfaction questionnaire. Effectiveness of the online games in relation to English vocabulary knowledge at B1 level was measured by the IDLL mini pretest and posttest. Quantitative data were analyzed by descriptive statistics, arithmetic mean, t-test dependent sample, and the effect size (ES) on learners’ gained scores. Qualitative data were investigated using content analysis of the IDLL evaluation form and learners’ satisfaction questionnaire. The findings revealed that the developed online games appear to be good learning material since both experts and learners view the games at the acceptable quality with sone revision needed further, such as learning content, game design, website design, technical limitation, and human errors. The findings also suggested that the developed online games were effective since students’ vocabulary knowledge had improved. Furthermore, students reported that they felt positive about the online games since they felt that they could learn in an enjoyable and interactive environment. Therefore, it was considered that this method can be used for teaching vocabulary within the Thai context. However, further development and revision were needed since the study was a trial process.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".