The Use of the KWDL Technique in Developing Grade 4 Elementary School Student Combined Operations
Bibliographic record
Abstract
The ability to recognize one’s learning processes is important for learning mathematics as it helps learners to practice the thinking process of how each element works in solving mathematics problems. The K-W-D-L technique has emerged as a technique that could bring about metacognition in learning and it could be beneficial in elementary school mathematics education. The objective of the study was to examine the effectiveness of the K-W-D-L technique on the learning achievement of fourth-grade elementary school students in combined operations. The participants grade 4 students in a public school in the Thai educational context. They were cluster sampling methods. The instruments were a K-W-D-L learning management plan, a learning achievement test, and a mathematic problem-solving test. The statistics used in data analysis were percentage, mean score, standard deviation, one sample t-test, and effectiveness index. The results of the study indicate the effectiveness index of the learning management plan designed using the K-W-D-L technique reached the determining criteria. Moreover, both participants’ learning achievement and mathematic problem-solving abilities were found to be at the desired level of the class.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".