Development of Non-Formal Education Program Enhancing Media Literacy for Families
Bibliographic record
Abstract
Media literacy has become an essential skill for all age groups in today’s complex digital environment. This study aimed to development of non-formal education program enhancing media literacy for families, based on a qualitative case study analysis in Thailand. Eight diverse families were selected and interviewed in-depth to examinevtheir media usage behaviors, experiences with misinformation or online scams, and family communication patterns regarding media. Thematic analysis of the interview data revealed several key factors contributing to media literacy at both personal and family levels. These included overreliance on a single familiar medium, lack of critical analysis and verification of information, overtrust in media content and endorsements, and insufficient communication within the family about media-related issues. Many family members who fell victim to media scams admitted that they did not cross-check information or discuss decisions with others beforehand. Based on these insights, a non-formal education program for families was designed. The program emphasizes collaborative learning among family members, covering core media literacy skills of access, analysis, and evaluation of media content. It consists of interactive learning activities that engage both parents and children in analyzing media messages, sharing experiences, and practicing safe media habits. The findings highlight that empowering families with media literacy and encouraging open intra-family communication can build a first line of defense against misinformation and fraud. The paper discusses how the family-based program can be implemented and offers recommendations for educators and policymakers to support and sustain media literacy education in the family context. All in all, this research contributes a practical family-focused approach to strengthening media literacy, which is critical for safe and informed media consumption in the digital age.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".