Building Bridges
Bibliographic record
Abstract
Supporting lifelong learning can be challenging in that participants are often geographically distributed, have significant time constraints, and widely varied skills and preferences with regard to technology. This creates the need for designers to support flexible configurations of systems for delivering content, in ways that still allow for meaningful learning and instruction to take place. In this chapter, the authors present a case study of experience in offering a university course using a novel system that bridges videoconferencing and webcasting technologies. These have historically been separate. Webcasting scales easily to accommodate large audiences, but only supports one-way transmission of audio and video. Videoconferencing allows for two-way interaction in real time, but uses more bandwidth, and does not scale as easily. Our system allowed for increased participation in webcasts, which had benefits for both instructors and students. This chapter presents an analysis of interaction and awareness in distance learning contexts, and concludes with design principles suggesting that designers of future systems focus on: (1) developing novel displays and visualizations for presenting information about students, (2) reducing inequalities between modes of participation by making it clearer when, say, questions are asked by text or who is speaking when there are multiple images displayed, and (3) accommodate a range of student preferences and capabilities by supporting multiple modes of presentation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.116 | 0.036 |
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".