Bibliographic record
Abstract
The article titled “Combining the Best of Online and Face-to-Face Learning: Hybrid and Blended Learning Approach for COVID-19, Post Vaccine, & Post-Pandemic World” authored by Dr. Jitendra Singh et al., presents a research study that creatively examines the history, evolution, and development of blended learning in a variety of educational settings. The article describes the review of various models of hybrid learning and different e-learning models currently used in higher education. In addition, the article articulates a number of issues that students, faculty, schools, and institutions of higher education faced during the onset of the COVID-19 pandemic in early 2020. Finally, the authors examine effective strategies for integration of best practices moving forward in the pandemic trajectory. Using a fishbone analysis, challenges faced by instructors and academic institutions are examined to lay the foundation for potential solutions and strategies. A detailed Strength–Weakness–Opportunities–Threat (SWOT) analysis of blended and hybrid mediums of instruction is described. The authors conclude with an evidence-based approach from their own experience and research which can be utilized by faculty and administrators worldwide, as we enter a post-vaccine and post pandemic world.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".