Bugs 101: Insect–Human Interactions; Developing and Implementing a General Entomology MOOC (Massive Open Online Course)
Bibliographic record
Abstract
As advances in technology continue to permeate the educational world, entomologists look for new ways to reach students and make our field more accessible to the public. One way to do so is through Massive Open Online Courses (MOOCs): courses from well-recognized universities that are available online, free of charge, to people all around the world. Since the creation of the first MOOC in Canada in 2008 (Baturay 2015), many leading universities have created courses launched on a variety of different online platforms. North American platforms include not-for-profits like Udacity, edX and Udemy, and for-profit platforms such as Coursera (Baturay 2015). European MOOC platforms include FutureLearn in the United Kingdom, iversity from Germany (Baturay 2015), and FUN-MOOC from France. At the University of Alberta, we launched our first MOOC, Dino 101: Dinosaur Paleobiology, in 2013 (McGreal et al. 2015), and we now offer seven full courses (10–12 lessons), each equivalent to a one-semester university-level course. We also offer two multi-course MOOC programs and five mini-MOOC courses containing four to five lessons each (www.ualberta.ca/admissions-programs/online-courses). The University of Alberta MOOCs are offered through the Coursera platform (coursera.org), giving our courses a reach of more than 120 countries around the world. Bugs 101: Insect-Human Interactions launched 28 June 2019 as the most recent full course in the University of Alberta’s ever-growing MOOC collection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".