The Effect of Computer-Assisted Educational Games on Teaching Grammar
Bibliographic record
Abstract
Discussions on how grammar should be taught have continued for decades. Previous studies have reported that today’s students called as Generation Z have shown negative attitudes toward grammar teaching with traditional methods and techniques, and that their academic achievements have failed to meet expectations. Not using methods and techniques that are consistent with the adopted philosophy of education hinders the success of this process. The study investigated the impact of computer-assisted instruction and correspondingly computer-assisted educational games on grammar academic achievement and attitudes toward grammar and Turkish course of students. In this study, a quasi-experimental design based on a quantitative study with a pretest-posttest nonequivalent group was applied. Participants of the study consisted of two classes of 6th grade students studying at a middle school. Computer-assisted educational games were designed and practiced in the experimental group within a 12-week period. For the control group, activities in the curriculum were followed during lessons. Results showed that grammar academic achievement of students between the experimental group in which computer-assisted educational games were practiced and the control group in which the existing curriculum was followed showed a significant difference in attitudes toward Turkish course and grammar on the behalf of the experimental group. Findings demonstrated that this kind of practice in teaching grammar made a significant difference on achievement and attitude of students. In addition, there was a positive, moderate and statistically significant relationship between attitudes toward grammar and Turkish course. Attitudes toward grammar of students determine attitudes toward Turkish course of students.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".