Innovative Teaching: Sharing Expertise through Videoconferencing
Bibliographic record
Abstract
Instructors in higher education commonly arrange for guest lecturers whose areas of expertise are related to the course content to give presentations to their classes. In doing so, the guest provides a perspective that differs from the on-site instructor's view and further enhances student knowledge. However, these experts are dispersed around the globe, and most university teaching budgets limit such invitations to those opportunities occasioned by the expert's coincidental proximity to the institution. Despite these geographic and financial obstacles, we believe that expanding the practice of inviting researchers and practitioners to share their expertise with students should be an important feature of teaching in the 21st century. New advances in communication technologies, which have already begun to have an impact on education at schools, colleges, and universities (O'Sullivan 2000), hold the promise of overcoming such obstacles. Collaborative learning, an increasingly utilized educational approach to teaching and learning that builds knowledge through interaction, is supported by new and emerging network collaboration technologies that have been promoted by many educational institutions (McInnerney and Roberts 2004). In the Department of Tourism Studies at Brock University, Canada, we were particularly interested in a technology solution that would permit guest speakers, often on another continent, to lecture to a local class. One such technology that we evaluated was communication through videoconferencing. Extraordinarily positive feedback from
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".