Android Based Material to Teach Early Reading for Primary Students using Construct 2 Apps
Bibliographic record
Abstract
Android based teaching material can facilitate students and teacher in the inside and outside classroom learning using smartphone. This study aims to investigate the ways in utilizing construct 2 apps to create android based material to teach early reading for primary students. This study is qualitative research in form of explorative case study which includes students in private and public primary schools at five regencies of Indonesia. The research data is collected using questionnaire and interview. It is analyzed using content analysis. The results of study show that android based material of early reading can be created by utilizing three steps. The conclusion of this study is that android based material of early reading is firstly created by investigating the need of material for primary students. Secondly, it creates the concepts of materials by preparing the material compositions, creating material order, and creating teaching material tools. Thirdly, construct 2 apps is utilized to make the teaching materials based on the concepts by creating parts of learning guide, invitation for praying, materials, evaluation, and games.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".