Challenges and Transformation of Pedagogy Towards Blended Learning: A Sequential Mixed-Method Study in Higher Education
Bibliographic record
Abstract
Abstract Two essential components, a robust information technology (IT) infrastructure and faculty training in student-centred pedagogies and technology usage, are necessary for effective blended learning designs. Many universities invest in IT infrastructure such as bandwidth, high-end subscriptions, servers, SMART boards, projectors, Wi-Fi enhancement, learning management systems, IT support, and other tools. Faculty training is crucial and includes instruction on using the new infrastructure and adopting pedagogical methods associated with blended learning. This study’s primary objective is to explore the challenges and pedagogical transformation towards blended learning designs in India. The research also investigates the impact of social context and emotional support on blended learning. It examines the mediating role of technostress among teachers between hybrid mode transformation and blended learning. The study’s results will provide critical insights for academic institutions’ higher management to encourage the adoption of learning designs and blended techniques by their employees during unforeseen events in the future, utilizing effective leadership and management skills. The study aims to assist academic institutions in meeting the demand for experiential learning in the classroom by incorporating blended learning. It acts as a bridge between industry expectations and academic outcomes. The study uniquely addresses the need for increased student engagement 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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".