A Structural Equation Model of Blended Learning Culture in the Classroom
Bibliographic record
Abstract
Higher education institutions are increasingly recognizing the importance of learning culture as a core factor for students' sustainable learning and development. While the development of blended learning environments in higher education institutions has been steadily increasing in recent years, but how to establish a blended learning culture in the classroom? The above problem can be solved when this study achieves its purpose to explore the factors of blended learning culture in the classroom. The focus of this study was to explore a structural equation model (SEM) of blended learning culture. A case study at the Hanoi University of Science and Technology (HUST), Vietnam was conducted and collected with a sample size large enough (339 students). The results of factor analysis have explored the core factors of the blended learning culture. The SEM analysis has achieved a first-order model of blended learning culture. And lastly, the SEM-values analysis for the existence of blended learning culture in the classroom has confirmed that they positively impact the acceptance of blended classrooms by students. Thus, a SEM of blended learning culture has provided a functional framework for educators to systematically cover all that create the success and sustainability of blended classroom culture 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".