A Blended Learning Future: COVID-19 Lessons for “Phygital” Higher Education
Bibliographic record
Abstract
The pandemic transformed higher education, making it clear that the future of education lies in the use of technology. Recognizing this development, this study examined the blended learning experiences of students and teachers during the COVID-19 pandemic in order to propose targeted strategies for the evolving “phygital” (physical + digital) university ecosystem. Drawing on existing literature, it explores three critical dimensions of the educational experience: technology, the teaching-learning process, and social interaction. Following a transcendental phenomenological approach, the study used a convenience sample of 10 students and 10 teachers, selected based on the saturation criterion. Using Leximancer software for text analysis, in-depth interviews with a representative sample of students and teachers were conducted. The findings exposed significant challenges faced during the pandemic, including a lack of digital tool proficiency among users, inadequate engagement with online content, organisational hurdles, increased workload, diminished personal interactions, and emerging mental health concerns among students. These insights underscore the urgency of crafting tailored strategies to enhance the phygital learning environment, focusing on improving infrastructure and providing comprehensive training to both students and educators.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".