Electronic Flipped Classrooms as a Solution to Educational Problems Caused by COVID 19: A Case Study of Research Course in Iran Higher Education
Bibliographic record
Abstract
A review of the related literature shows that flipped learning has greatly affected the students’ academic progress. However, despite a large number of studies on different forms of electronic learning, electronic flipped classrooms and traditional electronic(virtual) learning have not been compared to date. This study was an attempt to investigate the impact of traditional electronic, text flipped, and video flipped learning on improving the graduate students' theory and practical knowledge of research methodology. To meet the goal, the researchers employed a quasi-experimental research method, which is quantitative. The researcher selected three intact classes consisting of 48 postgraduate students majoring in social sciences and communication sciences and exposed each class to one form of electronic learning. The findings showed that flipped classrooms were more effective than traditional electronic learning, and text flipped learning was more effective than video flipped classes. The findings can be used by universities as well as university teachers to use electronic flipped classes as an alternative form of electronic learning It can be concluded that the universities need to encourage flipped classrooms in graduate and postgraduate courses as far as the universities can offer face-to-face classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".