The Development of the Digital Identification Instrument for Children with Learning Disabilities using Decision Support System (DSS)
Bibliographic record
Abstract
The study is a part of research and development which aims at developing Decision Support System-based (DSS) digital identification instrument for children with learning disabilities. The first-year study consists of three stages: (a) the need analysis of the instrument, (b) the development of instrument prototypes, and (c) the validation of the digital identification instrument. The study was conducted in Surakarta, particularly in 20 special schools located in 7 regencies and cities and selected using purposive sampling. In the first stage, data were collected using a close-ended questionnaire from 32 respondents comprising principals and teachers. Meanwhile, the second stage use of a web-based digital application development technique. The identification instrument was then validated through expert judgment using focus group discussion (FGD) technique involving information and technology (IT) experts, special education experts, principals, and teachers of children with learning disabilities. The instrument prototypes were subsequently revised and limited empirical tryout, and then analyzed using statistical tests. The results indicate that 97% of the respondents require the development of a digital identification instrument for children with learning disabilities. The study has successfully developed digital identification instrument prototypes for children with learning disabilities. All items of the DSS-based instrument have met the required criteria of validity: r-table with the number of subjects of 32, a significance level of 5% (0.361), and greater r-count compared to r-table (0.361). The reliability tests demonstrate Cronbach's alpha of 0.875. It's proved that 13 items of the instrument have a sufficient level of reliability.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".