Integrating web applications to provide an effective distance online learning environment for students
Bibliographic record
Abstract
Abstract The Human Physiology online course offered by the Department of Physiology at the University of Toronto ( www.physiology.utoronto.ca ) offers a quality online learning experience and promotes flexibility to its students in terms of time and location, allowing self-directed learning within a semi-structured frame-work. The online course population has expanded, including a more heterogeneous group of students. In addition to the traditional pre or current healthcare professionals (postsecondary students), there are now international students, working adults seeking career advancements, teachers, and even those just taking the course for personal interest. The course aims to use web tools to support and increase accessibility for all of these educationally and socially diverse students. Course material for students consists of 51 didactic lectures delivered in a video format (available to students for 24 hours, each day of the week for streaming) and a virtual lab experience. There are several sources of course support for students such as a 24/7 discussion board that is monitored by instructors and teaching assistants (an academic and peer support network), virtual tutorials with a teaching assistant (java applet chat) and instructors are always available to students by email. Frequent online quizzes were another feature that was very effective in both enhancing learning experience and improving student performance. Analysis of student data, student surveys and course evaluations from the online course suggested it was just as, if not more effective than the in-class course equivalent. The framework of this course can be easily adapted in creating an online course in any post-secondary discipline.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".