What influences computational thinking? A theoretical and empirical study based on the influence of learning engagement on computational thinking in higher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As an important part of core competencies in the 21 st century, computational thinking has received a lot of attention from all over the world. In the field of higher education, cultivating the ability of computational thinking has become an important goal of teaching. Previous research has shown that students' learning engagement is related to partial dimensions within computational thinking. However, there was a lack of research on the overall relationship between learning engagement and computational thinking. Therefore, this study aims at constructing an overall relationship model between learning engagement and computational thinking to examine the influence of three dimensions of learning engagement on the five dimensions of computational thinking. The participants were 341 freshmen from central China. The results show that compared with behavioral engagement, both emotional engagement and cognitive engagement had a stronger predictive power for computational thinking. In addition, the learning environment played a significant role in the relationship between learning engagement and computational thinking. On the whole, when compared with traditional multimedia classrooms, the relationship between learning engagement and computational thinking in smart classrooms was closer. A theoretical and empirical study of the relationship between learning engagement and computational thinking presents researchers and education practitioners with a method to improve students' computational thinking by building a learning environment and designing pedagogy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it