The Improvement of Attitudes toward Convergence of Preservice Teachers: Blended Learning versus Online Learning in Science Teaching Method Courses
Bibliographic record
Abstract
There has been a growing need as preservice teachers develop competencies regarding convergence. Focused on a discussion of blended learning before the COVID-19 pandemic versus online learning in the epidemic, we aimed to explore whether preservice teachers’ attitudes toward convergence can be influenced by the learning environment. Participants were a total of three hundred preservice teachers who attended the science teaching method courses training their TPACK at a teachers college in South Korea during the 2018 to 2020 academic years (194 in the blended learning group and 106 in the online learning group). Survey data on five subcomponents of attitudes toward convergence were collected at the start and end of the courses and analyzed using ANOVA and ANCOVA. As result, preservice teachers’ responses to the attitudes toward convergence in the pretests have a significant difference, whereas the overall scores in the posttests revealed no significant difference in the modalities of learning environments. Consequently, the preservice teachers engaged in the courses enhanced positive attitudes toward convergence regardless of delivery methods either blended learning or online learning. This paper provides evidence that the two teaching modalities of curriculum studies have the potential to foster preservice teachers’ attitudes toward convergence. This study supports that the blended and online learning formats of the course were feasible to induce short-term improvements in bias affective domains under the learning environments of science teaching method courses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".