TRANSITIONING TO ONLINE TEACHING IN AN UNDERGRADUATE APPLIED ENGINEERING COURSE
Bibliographic record
Abstract
The online teaching framework presented in this paper was developed for a third-year engineering design course at the University of Toronto, with more than 100 students located in 8 different time zones. The study was inspired by the pressing challenge faced by educators around the world in 2020 to fully transition to online teaching in a limited time, due to the COVID-19 pandemic. Different facets of the course were restructured to enhance the learning experience of students, overcome the challenges associated with online instruction, and implement active learning techniques in the course whenever possible. This initiative included the development of interactive course notes and a course map, with links to videos, 3D models, asynchronous lectures, and short video explanations. Several online paltforms were used in the framework, which are discussed in detail. The proposed framework provided an alternative to achieve the desired teaching outcomes in an online teaching environment for an engineering design course which had always been taught in-person prior to the year 2020. Despite the inherent challenges of this transition to online teaching, this format allowed for active learning activities in the course, promoting learner-centered teaching, and strengthening the students’ collaborative skills. Elements of the framework and the methodology outlined in this paper could be useful for other engineering courses attempting an effective transition to online instruction.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".