MULTI-CAMPUS TEACHING IN A CANADIAN ENGINEERING CONTEXT: ASSESSING PRESENCE USING THE COI SURVEY
Bibliographic record
Abstract
Multi-campus synchronous teaching using teleconferencing involves teaching to a class in-person and remotely, simultaneously. As an approach to postsecondary learning, it can offer students greater variety, access to remote experts, and opportunities to collaborate across regions. There are significant challenges to successfully managing a multi-campus course, where ongoing observation and evaluation of student experience is important in guiding pedagogical practice. Herein we explore learning experiences of students who attended a course taught in a multi-campus format as part of a newdual-campus engineering program offered at the University of British Columbia. We chose a Community of Inquiry (CoI) surveying tool to assess student experience by examining their perceptions on teaching, social, and cognitive presence at both campuses. Data collected and analyzed with a Multivariate Analysis of Variance show a clear disparity between perceptions of Teaching Presence between the two campuses, with significance in both the Design & Organization and Direct Instruction CoI subcategories. The ease of performing a CoI survey and assessing its results renders this approach to continuous improvement feasible for regular evaluation and continuous improvement within the Bahmani and Hjelsvold conceptual framework for multi-campus coursedevelopment. The study was undertaken as part of continuous improvement within the engineering program, with results used to develop and inform multi-campus synchronous teaching best practices in a Canadian engineering context.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".