Implementation of a Curriculum to Enhance Learning Management Competency in Computational Thinking for the Lower Secondary Teachers
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".