Eating Your Lectures and Having Them Too: Is Online Lecture Availability Especially Helpful in "Skills-Based" Courses?.
Bibliographic record
Abstract
At the University of Toronto at Scarborough, we provide enhanced flexibility to our students using a blended learning approach (i.e., the webOption) whereby classes are videotaped as they are offered in a traditional manner, then posted online for subsequent student access. Students can attend lectures live, watch them online at their convenience, or both. Previous research examining the webOption in the context of Introductory Psychology revealed that (a) students were satisfied with the webOption in general, (b) students used and appreciated the pause and seek features afforded by the webOption interface, and (c) those who used the pause and seek features performed slightly better on exams (Bassili & Joordens, 2008). The current research examines similar issues in the context of two mathematics courses. These courses differ from the lecture-based Introductory Psychology class in their emphasis on the teaching of mathematical proofs; cognitive skills that, like any other skill, are enhanced with practice (Schneider & Shiffrin, 1977). Access to online lectures allows students to re-experience the professor as they teach these skills. Given this, the webOption might be especially potent in these learning contexts. Surprisingly, the results we report here do not confirm that prediction. Students do use and appreciate the features of the webOption as was the case in our previous work, but those students who augmented their class attendance with online viewing, and those who used the lecturecontrol features the most, were actually the students who performed most poorly. Said another way, those students who had the most trouble with the course did indeed use the webOption as a way of understanding the material better but, interestingly, doing so did not result in better performance. Several possible reasons for this surprising result are considered.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".