The relationship between self-efficacy and computational thinking skills of fifth grade elementary school students
Bibliographic record
Abstract
Self-efficacy and computational thinking skills are necessary in this technological development age. However, only some studies still discuss the relationship between the two variables. The purpose of this study is to determine the form of relationship between self-efficacy and computational thinking skills of fifth-grade elementary school students. This study applied correlational quantitative research without accompanying the treatment of the subjects. The respondents for this study were 84 fifth-grade students from three public schools in Pekanbaru. Two types of instruments are used in this study, including questionnaires and computational thinking skills tests. The results showed a correlation coefficient of -0.036 with Sig. (2-tailed) 0.747 > 0.05. That is, there is a very low relationship, it has a negative direction, and it is not significant between self-efficacy and computational thinking skills of fifth-grade elementary school students. Self-efficacy contributes to the influence of computational thinking skills by only 0.12%, and other factors influence the remaining 99.88%. This study is expected to provide an overview of self-efficacy and computational thinking skills of fifth-grade students in Pekanbaru and is expected to be an additional reference for further study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".