Using Communicative Games in Improving Students’ Speaking Skills
Bibliographic record
Abstract
The aims of the study are to know whether communicative games have an impact on teaching speaking skill and describe how communicative games give an influence on speaking skills of students at junior high schools in Jakarta, Indonesia. Classroom Action Research (CAR) was implemented based on Kurt. L model. The procedures used were planning, acting, observing, and reflecting. It was done into two cycles in each cycle consisted of three meetings. The researcher used collaborative action research with some of the English teachers. In collecting the data, the instruments were interview, observation, questionnaire and test. The test only given to students. The rest of the instruments administered for both teachers and students. The result of the study showed the mean score’s pretest reached of 60.42 to 69.02 and post test’s score reached up to 78.77. It is important to describe that there is a significant improvement of 13.9% to 41.7% in post test 1 and 83.33% in post test 2. Therefore, the criteria of success had been determined. It is crucial to note that communicative games have contributed a positive impact on teaching learning process. This also implies the communicative games expected to enhance students’ enthusiasm and motivation. Clearly, It gives positive improvement on students’ active participation, confidence and their fluency in speaking skill. In short it can be described that the strategy of teaching and learning creates good, enjoyable circumstances and reduces the boredom and stress of learning process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".