Effects of Robotic Coding on Computational Thinking Skills of Secondary School Students
Bibliographic record
Abstract
In recent years, computational thinking has garnered increased attention as an essential problem-solving skill. One of the methods to develop students’ computational thinking skills is robotic coding activities. This study sought to investigate the impact of robotic coding activities on the self-efficacy perceptions of secondary school students’ computational thinking skills. A one-group pretest-posttest quasi-experimental design was employed, involving 32 secondary school students. These students, organized in groups of four, engaged in hands-on robotic coding activities using Lego Mindstorms EV3 Education robots over a total of 20 hours. Data were collected before and after the robotic coding activities using the Self-Efficacy Perception Scale for Computational Thinking Skills (SEPSCTS) instrument, comprising 36 items categorized into five factors. The data were analyzed using paired samples t-tests and analysis of covariance (ANCOVA). The results demonstrated a significant increase in students’ self-efficacy perceptions of computational thinking skills following the activities, with this increase observed consistently across genders. Finally, the challenges encountered during research and practice were reported, along with the study’s limitations, to inform future research endeavours.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".