Determinants of Teacher's Attitude towards Online Teaching and Learning
Bibliographic record
Abstract
Faculty attitude is an important aspect in determining their readiness for online education. This study seeks to understand the attitude of teachers in higher education regarding online teaching and learning. There were 759 participants from different colleges and universities in India (92 professors, 73 associate professors, and 594 assistant professors). This study was completed during the lockdown owing to Covid-19 outbreak. After reviewing relevant literature, data was initially gathered using Google forms based on the "Attitude Scale towards Online Teaching and Learning for Higher Education Teachers". Scale reliability was verified with the Cronbach Alpha and split-half reliability. Interrelationships between the constructs of attitude were examined using PLS-SEM. The study also revealed the existence of parallel and serial mediations between the constructs. It was established that knowledge could lead to responsiveness only in the presence of appreciation and proficiency. Hence, appreciation and proficiency are important constructs for teachers' responsiveness towards online education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".