The Effect of Preservice English Teachers’ Design Thinking on Their ICT Competencies in Hebei: The Mediating Role of ICT Integration Self-Efficacy Beliefs
Bibliographic record
Abstract
This study explored the relationship between design thinking, self-efficacy beliefs for integrating information and communication technology (ICT), and ICT competencies of pre-service English teachers (PSETs) in Hebei Province, China. The convenience sampling is used, and participants included 350 PSETs enrolled in four teacher-training colleges in Hebei. Questionnaires were administered to the study participants using the Design Thinking Scale, ICT Integration Self-Efficacy Scale, and ICT Competencies Scale. SPSS25 and AMOS25 were used to analyze the data, and the conclusion of this study was that design thinking has a significant positive effect on ICT competencies, and ICT integration self-efficacy plays a mediating role in the effect of design thinking on ICT competencies. Based on the results of the study, a teaching reform model framework is proposed in conjunction with the English teacher-training program in Hebei Province, China, with a view to providing a reference for future English teacher-training program teaching reform.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".