Integrating KWL Plus Technique with Mind Mapping to Develop Reading Comprehension Skills of Thai 9 Graders
Bibliographic record
Abstract
The purposes of the study were 1) to examine the effectiveness of the KWL plus mind mapping learning management on Thai grade 9 students’ reading comprehension and 2) to study the participants’ satisfaction with the KWL plus mind mapping learning management. The study employed a one-group experimental design, with 25 students from a public school in Mahasarakham province participating. The research instruments included a KWL Plus mind mapping learning management plan, a reading comprehension test, and a satisfaction questionnaire. Data were collected through pre-tests, post-tests, and during the implementation of six sub-learning lessons. The effectiveness of the learning management plan was assessed using process-product effectiveness (E1/E2), post-test effectiveness (E2), and an effectiveness index (E.I.). The results indicated significant improvements in students' reading comprehension, with the learning management plan showing a high effectiveness index (E.I. = 0.82). Additionally, the students expressed a very high level of satisfaction (average x̄ = 4.85) with the learning approach. This study contributes to the field of reading development by providing evidence of the combined effectiveness of KWL and mind mapping techniques in enhancing language learning in similar educational contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".