Reflections on the Adaptation of a Postgraduate Degree in Water Management from In‐person to Remote Delivery
Bibliographic record
Abstract
Abstract In early 2020, the COVID‐19 pandemic spurred the rapid adaptation of university course delivery to an online format. Though in‐person delivery partially resumed in the Fall of 2021, future conditions may favor a return to, or addition of, remote delivery. It is therefore important for instructors, program directors, and institutions to capitalize on this learning opportunity and reflect on adaptation measures’ successes (and failures) to inform future online course design. The reworking of McGill University's Master of Science Program in Integrated Water Resources Management (IWRM) provides a case study to evaluate the adaptation of remote teaching of water resource management. Informed by the Community of Inquiry (CoI) framework with a focus on preserving transferable skills, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was used to evaluate the five core program components. This evaluation framework, which can be applied to most university programs, resulted in several widely relevant insights. For example, remote delivery can create opportunities for greater participation of international students as it eliminates the need for translocation costs. Likewise, a larger variety of guest speakers can participate remotely, giving students greater exposure to different water career paths and research perspectives, ultimately strengthening the program. However, several weaknesses pose threats to online learning. The standard in‐person lecture‐style format must therefore be amended to maintain engagement and facilitate student‐to‐student and student‐to‐instructor learning processes. Course components that can enhance the online experience include breakout rooms, discussion boards, frequent journals/feedback forms, online activities, breaks, virtual office hours, and multi‐media presentations.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".