Explaining the impact of blended learning on relevant factors in west Tehran Payame Noor University
Bibliographic record
Abstract
By advances in information technology and considering the fast pace of innovation in targeted technologies, blended learning with the aim of satisfying the needs of blended learning is composed of online learning and face-to-face learning.The aim of the present paper is to study the impact of blended learning on the relevant factors through a mixed method.The study is considered fundamental in terms of research methodology.The present paper is carried out on students of West Tehran Payame Noor University, Iran through a questionnaire.According to the results, it is concluded from the perspective of students that although blended learning is formed of several factors such as face-to-face learning and virtual learning, this type of learning has significant impact on its constituent elements as well as on relevant factors related to this type of learning.Finally, the effectiveness of blended learning, virtual and face-to-face learning in accordance on their factors were determined and assessed which ultimately led to conclusions and recommendations to advance research objectives.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".