Students’ Perceptions of Kahoot!, Gamification, and the Role of Instructor in Online French as a Foreign Language Classes in Jamaica
Bibliographic record
Abstract
Gamification is a pedagogical tool that has brought much value to teaching and learning, including foreign languages (FLs), and serves to keep students engaged and motivated. Tools such as Kahoot! have gained much traction in FL contexts, with many studies focusing on their relation to learner enthusiasm and participation. However, the available data focuses primarily on the role of the teacher. The authors sought to gain students’ perspectives of Kahoot! in online French classes and the role of the instructor in the process. Sixteen undergraduate students at the University of Technology, Jamaica responded to a questionnaire. The study used a mixed-methods research design. Qualitative content analysis was employed to analyze the qualitative data, while descriptive statistics were used to analyze the quantitative data. Major findings reveal that Kahoot! is beneficial to FL learning, as it helps to improve students’ vocabulary, grammar, pronunciation, and cultural competence. Kahoot! also aids in the application, retention, and reinforcement of knowledge. However, technical and internet connectivity issues can impact the game’s flow. The teacher is responsible for preparing and facilitating the game strategically, as well as reviewing the answers with the students in a manner that promotes metacognition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".