The Development of a Digital Literacy Assessment Tool for Thai Grade 10-12 Students
Bibliographic record
Abstract
The current study aimed to create and validate a digital literacy assessment tool's quality for students in Grades 10-12 within the Thai educational context, and 2) to develop T-score norms derived from the results of the digital literacy assessment tool for students in Grades 10-12 in this context. The study followed a research and development (R&D) approach, including content validation, pilot testing, and confirmatory factor analysis (CFA) for construct validation. The participants consisted of 1,590 Grade 10-12 students from schools under the Phetchabun Secondary Educational Service Area Office, Thailand. Content validity was assessed using the Index of Congruence (IOC), and construct validity was verified using confirmatory factor analysis (CFA). Item difficulty, discrimination indices, and reliability (KR-20) were also analyzed. The results showed that the assessment tool demonstrated strong content validity (IOC = 0.60-1.00), acceptable difficulty levels (0.20-0.80), and discrimination indices (0.22-0.74). CFA confirmed the six-component model with excellent fit indices. The tool’s overall reliability was 0.94, with component reliability ranging from 0.70 to 0.83. T-score norms were developed to interpret student performance. This study provides a systematically validated digital literacy assessment tool tailored to the Thai educational context, supporting effective measurement and development of students' digital competencies.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".