Development of An Indonesian Language Teaching Module Based on The iSpring Suite Application for Elementary School Students
Bibliographic record
Abstract
This research aims to develop an Indonesian language teaching module based on the iSpring suite application for class II elementary school students. This research method used research and development (R&D) with the ADDIE model, which includes stages: Analysis, Development, Design, Implementation, and Evaluation. Three elementary schools in West Sumatra conducted this research. Data collection used observation sheets, questionnaires, interviews, validation instruments, practicality questionnaires, and evaluation tests. This research data analysis technique collected all the necessary data, namely from the results of module validation, module practicality and module effectiveness, and the N-Gain test. The teaching module validity test results obtained an average score of 4.34 in the very Good category. The results of the practicality test of the teaching module, teacher, and student responses obtained an average score of 4.45 in the very practical category. The effectiveness test can be seen from the results of the student knowledge test before the pretest and posttest question difficulty level. The test results at three elementary schools showed that the modules and tests could be declared effective; the average N-Gain Score was 58.23 in the quite effective category. The teaching module has proven to be very good, practical, and quite effective.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".