Bibliographic record
Abstract
The advent and popularity of asynchronous online learning has somewhat obscured a standby technology developed over the last two decades. Interactive videoconferencing, sometimes called "interactive television," though not as glamorous and popular a topic at distance-learning conferences, is still alive and well at many institutions. Three or four years ago, many of us were led to believe that interactive television would go the way of the dinosaurs-everything would soon be in an asynchronous format or on individual desktops. There would no longer be any need for elaborately designed classrooms, networks, and operations staff. To date, this prediction has not come true. In fact, synchronous interactive television has experienced significant growth as newer, easier, and cheaper technologies allow institutions to reach more students with less resource investment. Faculty and students, while appreciating the convenience of asynchronous delivery, still express a need for synchronous communication. This article explores the issues involved in synchronous distance education, the current technologies and proposed future developments, and best practices in terms of classroom design, faculty use, and operational issues. It is not a research article but an anecdotal case study based on Washington State University's experiences over the last 20 years in developing and adapting to new synchronous technologies and creating the support and technical infrastructure to best deliver academic courses through this medium.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".