Astronomy for astronomical numbers: A worldwide massive open online class
Bibliographic record
Abstract
Astronomy: State of the Art is a massive, open, online class (MOOC) offered through Udemy by an instructional team at the University of Arizona. With nearly 24,000 enrolled as of early 2015, it is the largest astronomy MOOC available. The astronomical numbers enrolled do not translate into a similar level of engagement. The content consists of 14 hours of video lecture, nearly 1,000 Powerpoint slides, 250 pages of background readings, and 20 podcast interviews with leading researchers. Perhaps in part because of the large amount of course content, the overall completion rate is low, about 3%. However, this number was four times higher for an early cohort of learners who were selected to have a prior interest in astronomy and who took the class in synchronous mode, with new content being added every week. Completion correlates with engagement as measured by posts to the online discussion board. For a subset of learners, social media like Facebook and Twitter provide an additional, important mode of engagement. For the asynchronous learners who have continuously enrolled for the past 15 months, those who complete the course do so quickly, with few persisting longer than two months. The availability of a free completion certificate had no impact of completion rates when it was added midway through the period of data analyzed in this paper. This experiment informs a new offering of an enhanced version of this MOOC via Coursera, along with a co-convened “flipped” introductory astronomy class at the University of Arizona, where the video lectures will be online and class time will be used exclusively for small group labs and hands-on activities. Despite their typically low completion rates, MOOCs have the potential to add significantly to public engagement with science, and they attract a worldwide audience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".