How Does Gamified L2 Learning Enhance Motivation and Engagement
Bibliographic record
Abstract
In recent years, the popularity of gamification has gained momentum with the growing numbers of publications as well as the mass appeal among learners for its potential to stimulate motivation, engagement, and positive experiences. However, this vein of research has mainly focused on the effects of game mechanics and how they can be incorporated within a gamified learning context to enhance users' positive experiences. In L2 teaching and learning, the literature states that most studies on gamification lack theoretical principles that can guide the design of gamified learning experiences that promote learners' motivation and engagement. To make the picture more coherent, this chapter synthesizes the existing literature on gamification L2 learning, focusing on empirical findings related to factors affecting L2 learning, current L2 gamified design models, gamification affordances, and their inherent motivational and engagement outcomes. For this review, thematic and content analysis of 73 publications dating from 2017 to late 2022 were examined.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".