Exploring Chinese Elementary Teachers’ Perceptions and Implementations of Gamification in Online EFL Classrooms
Bibliographic record
Abstract
This study investigates Chinese elementary teachers’ perceptions and implementations of gamification in online English as a Foreign Language (EFL) classroom. Using a mixed-methods approach, data were collected through a questionnaire (N = 140) and semi-structured interviews (N = 7). Teachers’ perceptions of gamification were analyzed across three dimensions: technological, cognitive, and pedagogical. Technologically, the results revealed that most teachers perceived gamification tools on the ClassIn platform as user-friendly and engaging, although limited in variety. Cognitively, teachers recognized gamification’s potential to enhance motivation and engagement, but expressed concerns about its potential to distract students. Pedagogically, while gamification was perceived as a complement to traditional teaching methods and a means to foster student-centered learning, it posed challenges such as increased workload and difficulties aligning activities with academic goals. Regarding implementation, gamification was most frequently used in vocabulary and reading activities, whereas its use in writing and listening activities was limited due to higher cognitive demands. Key challenges included managing students’ negative emotions from competition, addressing parental skepticism, overcoming technical and classroom management barriers, allocating sufficient time for effective implementation, and adapting gamification tools to diverse student needs. Key factors shaping implementation included student characteristics and content suitability. The results emphasize the need for tailored professional development, adaptive gamification strategies, and institutional support to maximize the benefits of gamification while addressing its challenges. This study offers actionable insights for enhancing teaching practices and improving student engagement in online EFL classrooms.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".