Bibliographic record
Abstract
Videoconferencing as a learning tool has been widely used among educators and learners in order to induce effective communication between learners and teachers or learners and their peers, especially when face-to-face means are not possible. Different types of videoconferencing platforms or systems have emerged for use in today’s higher education institutions. Previous research has focused on examining the potential of three different forms of videoconferencing systems: desktop videoconferencing (DVC), interactive videoconferencing (IVC), and Web videoconferencing (WVC). In this study, a review of the literature was conducted to increase the current knowledge regarding the use of these videoconferencing systems. A classification of the videoconferencing paradigms from the constructivism and cognitivism perspectives was provided. The summary of the results for these videoconferencing systems revealed specific learning opportunities, outcomes, and challenges for both learners and instructors. The results suggest that current policy and teaching strategies are not ready to provide an accessible and comprehensive learning experience in DVC and IVC. Relative to previously conducted studies regarding the use of videoconferencing in higher education, this study offers a broader consideration of relevant challenges that emerge when using certain videoconferencing systems in both learning and teaching situations.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".