The Impact of Gamification “Kahoot App” in Teaching English for Academic Purposes
Bibliographic record
Abstract
Incorporating gamification into foreign language learning is a successful and effective strategy for improving learners' level of language skills and helping them to master communication with others. This study aimed to determine the impact of Kahoot, a gamification-based technological application, on English language vocabulary for academic purposes and motivation. Thus, 60 students were selected from among the university students enrolled in the English Language for Academic Purposes course. These students were randomly split into two equal classes: the control and experimental groups. English vocabulary Test was given to both groups following a pre-test, with the experimental group receiving Kahoot instruction while the control group received traditional instruction. The teaching continued for ten weeks, after which the students were tested with a post-test on the same vocabulary learned. Moreover, a motivation scale was used to identify the effect of Kahoot in enhancing the students' motivation toward learning English for academic purposes. The results showed positive results on the extent of the positive impact (of the Kahoot learning tool based on gamification) in learning English vocabulary for academic purposes and 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".