Learning Achievement Improvement of 1st Grade Students by Using Problem-Based Learning (PBL) on TPACK MODEL
Bibliographic record
Abstract
The objectives of the research were: 1) to develop the lesson plans for “Weight and Measurement” of Mathematics by using Problem-Based Learning on TPACK MODEL based on the efficiency of the process and the overall result (E1/E2) at the established criteria of 75/75; 2) to compare the students’ learning achievement in “Weight and Measurement” of the 1st grade students before and after by using Problem-Based Learning on TPACK MODEL; 3) to study the students’ satisfaction with Problem-Based Learning on TPACK MODEL. The research samples were thirty-five 1st grade students of class 1 in the 1st semester of the academic year 2020 at Sanambin School in Khon Kaen Province. They were selected by purposive sampling. The instruments used in this study were lesson plans, an achievement test, and a questionnaire on students’ satisfaction. The statistics used for analyzing the collected data were mean, standard deviation, percentage, and gain score. The research results showed that 1) the average efficiency of the lesson plans for “Weight and Measurement” by using Problem-Based Learning on TPACK MODEL with exercises was 85.54/78.71, which was higher than theestablished criteria. 2) The mean score of the 1st grade students for “Weight and Measurement” of Mathematics after using Problem-Based Learning on TPACK MODEL was significantly higher than that of before using the Problem-Based Learning Model. 3) The overall satisfaction of the students with the Problem-Based Learning on TPACK MODEL for “Weight and Measurement was at a high level.
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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 | 0.001 | 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 | 0.001 |
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".