TEACHING LARGE ONLINE CLASSES: HOW CAN PROFESSORS PROMOTE ACTIVE LEARNING WITHOUT EXHAUSTING THEMSELVES?
Bibliographic record
Abstract
This 50-minute session featured a discussion about innovative teaching techniques in large online classes, and the associated challenge of keeping the workload manageable for instructors. What are the alternatives to multiple-choice exams? In large online classes (of 50-100 students), instructors often face students who feel “isolated” but who also ironically rarely take advantage of opportunities to engage with other students unless there is a grade associated. This can result in either a barrage of emails between individual students and the professor, or students who withdraw and refrain from asking questions and engaging. Online teaching also presents the instructor with an ever-evolving selection of unique online tools. While implying exciting possibilities for active learning, teaching pedagogy often follows at a slower pace. For example, how can one use real-time interactive tools in a large online class when it is impossible for all students to be online at the same time? I began this session by showing the session participants my two online-course Moodle websites and describing the assignments I have developed for these larger online classes. I discussed the ongoing dual challenge of incorporating innovative, active learning opportunities for students, while also attempting to keep the amount of marking and formative feedback required of me (the instructor) to a manageable level. Session participants asked questions and inserted their own comments and stories throughout this process. We also discussed additional teaching strategies for large online classes identified in the academic literature.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".