Components and Indicators of Computational Thinking Learning Experience Management Competency of Early Childhood Teachers in Educational Institutions
Bibliographic record
Abstract
This research aimed to: 1) investigate the components and indicators of computational thinking learning experience management competency of early childhood teachers in educational institutions under the Office of the Basic Education Commission, and 2) examine the congruence of these components and indicators. The research was conducted in two phases: Phase 1 involved studying the components and indicators of computational thinking learning experience management competency of early childhood teachers, and Phase 2 consisted of a confirmatory factor analysis of the competency in computational thinking learning experience management of early childhood teachers. The sample comprised 330 early childhood teachers from educational institutions under the Office of the Basic Education Commission in the northeastern region, determined using a 15:1 ratio of 22 parameters and selected through multi-stage random sampling. The research instrument used was a questionnaire designed to develop the components and indicators of the computational thinking learning experience management competency of early childhood teachers in schools under the Office of the Basic Education Commission. The data were analyzed using Confirmatory Factor Analysis (CFA). The findings revealed that: 1) The components and indicators of computational thinking learning experience management competency of early childhood teachers, synthesized from relevant documents and research, consisted of: (1) decomposition of problems into sub-problems/sub-tasks, (2) pattern recognition in problems or solution methods, (3) abstraction of essential problem elements, (4) algorithm design, and (5) unplugged programming, with a total of 17 indicators; 2) The alignment of the empirical data with the components and indicators of the competency showed that the chi-square value was 85.641, with 69 degrees of freedom (df), a chi-square/df ratio of 1.2411, a statistical significance (p-value) of 0.085, a Tucker-Lewis Index (TLI) of 0.994, a Comparative Fit Index (CFI) of 0.997, a Root Mean Square Error of Approximation (RMSEA) of 0.027, and a Standardized Root Mean Square Residual (SRMR) of 0.025.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".