MOTIVATING SECONDARY SCHOOL STUDENTS TO LEARN GRAMMAR THROUGH GAME-BASED STUDENTS RESPONSE SYSTEMS (GSRS) APPLICATION : STUDENTS’ PERCEPTION IN INDONESIA
Bibliographic record
Abstract
The present study aimed to perceive students’ perception in learning grammar through GSRS in secondary schooling context in Indonesia. GSRS can help the students to boost their engagement and motivation. Qualitative case study was used as research design. 12 students participated in this study. They were selected by using purposive random sampling. Questionnaire and interview were used to collect the data regarding students’ perception in using Kahoot application. The questionnaire comprises 20 questions and the interview consists of 9 questions. The collected data were analyzed quantitatively. The findings of the study showed that the students’ responses toward Kahoot! Application use in learning grammar were positive. The students felt enjoyable during the class (75%) and they were motivated to learn tenses or grammatical point (58,3%). Another finding found is Kahoot! could reduce the boredom of learning process in grammar class (58,3%). While Kahoot! Application provided positive impact on students’ motivation in learning grammar, the sudentts also experienced challenges in learning grammar during the class such as unstable network and limited time to response the questions. The study empirically shows that students’ perception toward Kahoot! application in learning grammar is positive. It can encourage secondary school students to learn grammar more enjoyable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".