Problems and Needs in Instructing Literacy and Fluency of Reading and Writing Skills of Thai L1 Young Learners
Bibliographic record
Abstract
The purposes of the current study were 1) to investigate problems in instructing literacy and fluency of reading and writing of Thai L1 young learners, and 2) to investigate needs in instructing literacy and fluency of reading and writing of Thai L1 young learners. There were 2 groups of participants including a group of 15 samples answering a questionnaire and a group of 10 samples taking part in an interview session. The instruments were 1) a questionnaire and 2) a structured interview to study problems in instructing literacy and fluency of reading and writing skills of Thai young learners and 3) a questionnaire and 4) a structured interview to study needs in instructing literacy and fluency of reading and writing skills of Thai young learners. The quantitative data were analyzed using percentages, mean scores, and standard deviation. Meanwhile, the results of the interview were analyzed by a qualitative analysis method. The results of the study show that 1) problems in instructing literacy and fluency of reading and writing skills of Thai L1 young learners are the learners’ knowledge in textual language systems in terms of spelling, meaning, and uses in both receptive and productive manners; 2) needs in instructing literacy and fluency of reading and writing skills of Thai L1 young learners rely on finding possible solutions to solve these problems considering the nature of young learners’ learning.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".