Teachers’ Technology Use and the Change of Their Pedagogical Beliefs in Korean Educational Context
Bibliographic record
Abstract
Many researchers have attempted to identify the factors that lead teachers to use technology in the classroom. However, few studies have examined whether and how teachers’ technology use change their pedagogical beliefs. Therefore, this study investigates how teacher-related factors influence teachers’ use of technology, and how changes subsequently occur in their pedagogical beliefs when they incorporate technology into their teaching practice. The study participants were 659 in-service teachers from elementary schools in South Korea. We employed structural equation modeling to determine the relationship between the intrinsic and extrinsic factors influencing teachers’ use of technology, as well as changes in their pedagogical beliefs. The results showed that an intrinsic factor, namely teachers’ attitude toward technology, mostly drives their use of technology in practice, and triggers changes in their beliefs. In addition, extrinsic factors such as the pressure to use technology and administrative support can affect teachers’ use of technology and changes in their beliefs. Finally, this study provides implications for teachers, school administrators, and policymakers that contribute to the discussion on technology integration in the classroom.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".