ICTs for non-formal education in rural Thailand
Bibliographic record
Abstract
Non-formal education (NFE) has a role to play in the education of marginalised groups such as out-of-school adults. NFE is based in the discourse of lifelong learning with its agenda of economic growth and active citizenship. This discourse requires moving beyond traditional conceptualisations of primary, secondary and tertiary education to conceptualise lifelong learning as formal, non-formal and informal. Information and communication technologies (ICTs) can potentially support NFE, but not enough is known about this potential. This study investigated ICT use in NFE in rural Thailand. The study compared collaboration, content knowledge and satisfaction in a Career Education course between students learning face-to-face (F2F) versus students learning F2F with desktop computers (F2F+DT). We compared the same variables in an English in Daily Life course between students learning F2F versus students learning F2F with mobile phones (F2F+M). Comparisons of the F2F and F2F+DT modes revealed no significant difference in content knowledge, in students’ perceptions of collaboration or in satisfaction. Comparison of the F2F and F2F+M modes revealed content knowledge and satisfaction were higher for the F2F+M mode but there was no significant difference for collaboration. Comparisons of F2F+DT with F2F+M revealed no significant difference for content knowledge or for satisfaction. The F2F+M mode was significantly higher for perceptions of collaboration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".