The Impact of Mobile Game-Based Language Learning Apps on EFL Learners’ Motivation
Bibliographic record
Abstract
This study examines the effect of integrating mobile-game based language learning applications (MGBLLAs) on Saudi female English as a Foreign Language (EFL) students’ motivation to learn English. It explores the perceptions of students regarding the pedagogical value of the following free MGBLLAs: Game books: Great Reader, Game to learn English - EnglishTracker, and Learn English Vocabulary Pop Quiz. A group of thirty Saudi female beginner level students, aged from 18-20 years old and enrolled for their foundation year at King Abdulaziz University (KAU) participated in the study. The study was carried out over a seven week period. Data were collected using two questionnaires. A pre-MGBLLAs integration questionnaire was modified to determine students’ motivations for learning English. A post-MGBLLAs integration questionnaire designed by the author was also issued. It was utilized to explore the perceptions of students regarding the use of the three mobile game-based language learning apps, and to discover any impact on learner motivation. The results of the pre-MGBLLAs integration revealed that the EFL students were motivated to learn English. However, their motivation was high instrumental motivation, because it is taught as a compulsory course in their foundation year and they must achieve high scores to be able to start studying their preferred major. Significantly, the findings of the post-MGBLLAs integration questionnaire revealed that students perceived the three apps as beneficial for learning and improving motivation. These results contribute to the literature regarding mobile game based learning, and EFL students’ motivation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".