Implementation of a Curriculum to Enhance Learning Management Competency in Computational Thinking for the Lower Secondary Teachers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Teaching computational thinking develops students in analytical thinking, systematic thinking, step by step reasoning to solve problems, applicable to real-life problems, and can be integrated across a wide range of disciplines, combining knowledge to create works and extend knowledge to other subjects. The objective of this study was to examine the outcomes of a curriculum to enhance learning management competency in computational thinking for lower secondary teachers. The samples were 4 teachers selected by purposive sampling, and 123 grade 8 students selected by the criterion of 70% from private schools under Mahasarakham Provincial Education Office, Office of the Private Education Commission, Thailand. The instruments for the lower secondary teachers were; 1) a test to measure knowledge and understanding of teachers' computational thinking learning management, 2) an assessment form for learning activity design ability, and 3) an observational form of learning management ability, while a computational thinking ability test was employed to the students. The data were analyzed by mean, percentage, standard deviation, and the Wilcoxon signed rank test. The results were; 1) the teachers after the workshop had higher knowledge and understanding of computational thinking learning management than before the workshop; 2) the teachers were able to design learning management that promotes computational thinking at a high level; 3) the teachers were able to provide learning management that promotes computational thinking: overall, the average was good; and 4) the students' computational thinking ability after learning was higher than before learning at a statistical level of .05.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it