Board 122: Preparing to Teach a Multi-Campus (Distributed Learning) Course
Bibliographic record
Abstract
In this theory paper, a review of best practices for preparing to teach a course in a Multi-Campus format.Multi-campus instruction (MCI), also known as distributed learning, is an instructional format that involves a single instructor in a classroom at one location (the "local" cohort) while synchronously teaching "local" and "remote" cohorts of students that are situated at other campuses.Students in the "remote" cohorts attend using Information and Communications Technology (ICT) such as video conferencing equipment.Courses and full programs offered in this format are becoming increasingly popular at educational institutions around the world.Resources exist to support instructors, but they can be difficult to locate, are limited in scope, or have not been updated to keep up with technological advances.Whereas other literature typically considers a theory-based framework focusing on educational pedagogy and philosophical principles for developing an MCI course, this paper examines practical considerations when offering courses across multiples campuses, with a focus on planning and administration.It identifies and lays out common considerations one must make when delivering an MCI course, including maintaining equity across cohorts, contextual differences across cohorts, content delivery and student activity planning, communication, IT resources, human resources (teacher's assistant, TA), and scheduling.Preferred presentation style: Traditional lecture
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".