Development of a Model of Individual and Contextual Factors Affecting Instructors’ ICT Literacy for Private Universities in Hunan Province of China
Bibliographic record
Abstract
This study endeavors to establish a model that encapsulates individual and contextual factors affecting instructors’ ICT literacy in private universities located in Hunan Province, China. The researcher employed a mixed-methods approach, integrating both qualitative and quantitative techniques, through a questionnaire survey administered to 555 instructors from private universities in Hunan Province. The findings reveal the following: 1) The level of instructors’ ICT literacy in private universities within Hunan Province is notably high. 2) Among the individual factors affecting instructors’ ICT literacy are ICT Self-efficacy and ICT Engagement, while contextual factors encompass University ICT Support and ICT Training. 3) The ICT literacy of instructors in private universities in Hunan Province is shaped by the intricate interplay between individual and contextual factors. Based on these insights, the study proposes a comprehensive model that integrates instructors’ ICT literacy with their ICT Self-efficacy, ICT Engagement, as well as the contextual factors of University ICT Support and ICT Training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".