Using Online Computer Games in the ELT Classroom: A Case Study
Bibliographic record
Abstract
The purpose of this research was to investigate the effectiveness of computer games in learning English as a foreign language and the extent to which they increase motivation in young students. More particularly, this research investigated the validity of the hypothesis that computer games are a particularly motivating means for young students to learn English vocabulary effectively in comparison to other approaches suggested by the Greek National Curriculum. The grade, in which this research was conducted, was the 4th grade of Primary school as it is a borderline grade in which greater demands are imposed on the students of this age group and language level regarding, mainly, their reading and writing skills as in this class, for the first time, it is explicitly stated by the national curriculum that literacy is one of the three basic axons upon which English language learning should be developed. All in all, the results of this research shed light on the effects which new technologies have on language learning as well as their ability to motivate students to learn English. The results of this research will also be used as a basis upon which specific suggestions for the practical implementation of computer games in the everyday classroom can be made.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".