Programming Skills and the Relation in Fostering Students’ Higher Order Thinking
Bibliographic record
Abstract
Programming skills (PS) refer to coding and debugging that are required for those who write a program in any programming language. Coding can be described as the implementation aspect of programming, whereas debugging can broadly be defined as fixing any incorrect code that is found after running the programming test. Higher-order thinking skills (HOTs) refer to the top three levels of Bloom’s taxonomy which are Analysis, Synthesis, and Evaluation. This study aims to determine the relationship between PS and HOTs among secondary students. Many studies indicate that students who attend programming courses for first time have low levels of performance in PS. Coding and debugging skills reflect higher-order thinking levels. Therefore, an objective of this study was to investigate the effect of coding and debugging skills on their HOTs. The benefits of having PS among learners are that they are able to achieve HOTs. Indeed, these relationships may be explained by programmers need to apply all these HOTs throughout the three phases of the programming process. Students who have low levels of PS are able to achieve the analysis level, while students who have moderate levels of PS able to achieve the synthesis level, and finally, students with high levels of PS are able to achieve the evaluation 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".