MOTHER TONGUE-BASED MULTI-LANGUAGE LEARNING IN READING: DEVELOPING PARENT INFORMATIONAL SHEET
Bibliographic record
Abstract
Getting data on the mother language for primary students can be challenging in a multilingual setting as Indonesia. There are around 700 spoken languages spoken in Indonesia. It is often challenging to assess a young learner first language directly because of the shortfall of assets accessible in each language. Getting information on every child's mother tongue acquisition is very important for bridging teaching instruction in primary years, as does in reading. This study's objective was to assess the validity and reliability of an adapted parent questionnaire on the first language development of Indonesian learners that is not specific to a particular language or cultural group. This research and development use a 4-D model (define, design, development, disseminate). The defined stage consists of focus group discussion resulting in the need for mother-tongue information to support instruction in reading comprehension. The design stage is the adaptation of the Alberta Language and Development Questionnaire (ALDeQ)'s existing questionnaire leading to the parent information questionnaire design fitted into the Indonesian context. Field tests and data analysis are conducted in the developmental stage. This descriptive quantitative research did not go through the dissemination stage because not being developed wider. The Gregory content validation formula obtained a score of 1, which was categorized as very high, indicating that the instrument is eligible. The Product Moment empirical validity indicates a high validity. Reliability tests using Cronbach's Alpha formula showed a value of 0.86 which means very high.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".