Problems and Needs Assessment to Learning Management of Computational Thinking of Teachers at the Lower Secondary Level
Bibliographic record
Abstract
The objective of the study was to investigate the problems and needs in the learning management of computational thinking among teachers at the lower secondary level in private schools in the province of Maha Sarakham, Thailand. This current study comprised 42 participants. The research tools were 1) questionnaires about problem situations in learning management for computational thinking and 2) recordings of group discussions. 1) The findings revealed that teachers had limited knowledge and understanding of learning management in computational thinking (xത = 2.43, S.D. = 0.44). In this regard, teachers believe that computational thinking is regarded as knowledge in addition to literacy, and they recognize that computational thinking, together with reading, writing, and calculating, is the cornerstone of learning in the 21st century. The best way to foster and develop teachers in teaching and learning computational thinking skills is through training and collaboration with the technology that should be used in teaching and learning computational thinking (i.e., computers, computer programs, smartphones, and multimedia). 2) Teachers indicated a strong need for self-improvement in terms of learning management in computational thinking (xത = 4, S.D. = 0.63). Through training, teachers want to improve their control of computational thinking. The development of learning management abilities that enhance computational thinking involves the following five steps: 1) Educating teachers; 2) Having a speaker or mentor instruct them in the creation of activities; 3) providing activities for teachers to practice together until proficiency is attained; 4) enabling each teacher to present the outcomes of the activities; and 5) teachers collectively summarizing the results of the activities.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".