Enhancing Virtual Teaching and Learning through Connectivism in University Classrooms
Bibliographic record
Abstract
It is argued that teaching and learning in the 21st century rely heavily on technology, especially in university classrooms. This theoretical paper contends that for students to be successful in university classrooms in the 21st century, both lecturers and students should effectively resonate with technology. This paradigm shift is not without one or two challenges which must be addressed since teaching and learning through technology has come to stay. Therefore, this study presents the proponent of connectivism theory to enhance virtual teaching and learning in university classrooms. The study is located within a transformative worldview and derives its argument from a theoretical viewpoint by positioning connectivism as a tool to enhance teaching and learning in 21st-century university classrooms. Conceptual analysis was employed to argue the place of connectivism as a tool to enhance virtual classrooms in universities. The connectivism theory was presented, and its assumptions were argued in relation to how it could be integrated into university classrooms. The study concludes that the diversity of nodes' interconnections, coherence of things and adaptation to constant change are dimensions that could enhance virtual classrooms. Therefore, concerted efforts of both lecturers and students in universities to improve these dimensions to transform virtual space in university classrooms.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".