Digital Game-Based Learning in Higher Education: ESL Teachers and Students Perceptions
Bibliographic record
Abstract
Digital game-based learning (DGBL) is a new approach in educational settings that aims to engage students, encourage curiosity, and provide a versatile learning experience. It has been integrated into educational settings due to the widespread use of digital games by students. This study examines the perceptions of integrating digital games into English as a Second Language (ESL) classroom among 89 second-year engineering students and 11 teachers at a private university in Chennai, India. Employing a mixed-methods research design, data were collected and analyzed using SPSS software. Unlike previous studies that focus solely on the positive aspects of DGBL, this research highlights both the benefits and potential drawbacks of integrating digital games into English language education. By comparing teachers’ and students’ perspectives, the findings emphasize the importance of careful selection and integration of digital games and reveal significant insights into the cognitive, affective, and social aspects of digital game-based language learning. These insights are crucial for guiding policy decisions, instructional practices, and resource development in language education. The study underscores the value of digital games as innovative educational tools and advocates for their thoughtful adoption of digital and technology-based teaching and learning in 21st-century in ELT practices can enhance multidisciplinary skill development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".