Bibliographic record
Abstract
The dominant complexities of English teaching in Indonesia are about limited development of teaching methods and materials which still cannot optimally reflect students’ needs (in particular of how to acquire knowledge and select the most effective learning models). This research is to develop materials with complete task-based activities by using CALL at junior high school in Indonesia. In order to entirely develop comprehensive materials, the first step is to do need analysis especially identifying task based learning model which fulfils students’ expectation, creates and develops pre task and task cycle (consisting of task, planning, and reporting) into CALL integration of learning module. The results of the students’ needs show that they like to have appropriate materials for their level, related to real life, variety of media and sources of learning chiefly incorporated to computer which optimizes interactive, contextual, and authentic materials. Therefore, the design of its prototype consists of three main steps. First, pre-task activity, it is designed in observing and questioning stage which is done by providing English comics with thematic situations. Second, main task activities, it is designed in exploring and associating stage which are done by presenting real life situations by utilizing videos, songs, and stories which elaborate exercises, role play, and discussion sections. Finally, post task activity, it is designed in communicating stage and it is intended to provide follow-up activities in wider contexts. The evaluation of the final product is done by validating the materials by means of a group discussion with English teachers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".