EAL Instructors’ Attitudes towards Game-based Learning Adoption in Education: Opportunities
Bibliographic record
Abstract
The objective of the current study is to explore teachers’ attitudes towards game-based learning (GBL) as well as opportunities in English as additional language (EAL) context. The study used a survey and interview to collect data. The survey data was gathered from eight university EAL teachers. Four of the survey respondents voluntarily participated in interviews to explore the opportunities of using GBL. The quantitative data was analyzed using descriptive statistics and the qualitative data was analyzed through thematic analysis and description. The findings of the survey data revealed that the factors of usefulness, attitude, and social influence contribute to the use of GBL. As a result of the interview data analysis, three themes associated with the opportunities of GBL emerged namely, communication, fun learning, and motivation. The implication of the study is that teachers acknowledge the benefits of GBL but they need support for further professional development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".