Effects of Gamification on Chinese EFL College Students’ Writing Error Awareness and Writing Performance
Bibliographic record
Abstract
English writing is an important course to improve students’ comprehensive use of language. However, in the current practice of English writing teaching, college students in China still have a variety of problems in writing English compositions. Therefore, how to further improve students’ English writing ability has always been the focus and difficulty of English writing teaching. Although the traditional Error Analysis Theory has systematically studied the common mistakes in college English writing, students’ awareness of common mistakes in English writing still needs to be further improved. The thesis attempts to explore the effectiveness of gamification learning in improving students’ error awareness and writing performance, as well as students’ perception of gamification methods and traditional teaching methods. The experimental results showed that: 1) Gamification learning can help improve students’ awareness of common mistakes in English writing. 2) Gamification learning has obviously raised students’ comprehensive writing results, of which the misformation errors have been improved most significantly, followed by the improvement of cohesion/coherence, content/organization and language. 3) Gamification has been accepted and praised by most students because the instructional content in gamification is more professional, targeted and meaningful, and game design is more interesting and challenging. This study attempts a new exploration to apply gamification into English writing teaching, and has obtained satisfactory results, which has provided not only pedagogical suggestions for English writing teaching, but also some enlightenment for the future studies of gamification in English writing teaching.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".