Bibliographic record
Abstract
UBC successfully delivered five massive open online courses (MOOCs) in the spring and summer of 2013. Individual MOOCs incorporated different pedagogical design strategies to achieve their desired learning outcomes and course objectives. The pilot MOOCs lasted from five to eleven weeks and provided tens of thousands of learners worldwide, including over 8,100 students who earned certificates for completing the courses, with an opportunity to engage with UBC instructors and learning materials. Development of these courses involved the creation of large amounts of new learning material, including more than 60 hours of video-based lectures, 98 text-based module pages, 1,040 quiz questions, the use of tools outside of the Coursera platform, and multiple innovative learning activities. Learning materials from the MOOCs have been, or will soon be, used by hundreds of UBC students in credit-bearing courses. Additionally, instructors made efforts to facilitate the reuse of their learning materials through the use of Creative Commons licenses and the transferring of content to additional platforms beyond the Coursera platform, such as external YouTube channels. The MOOC pilot supported UBC’s learner-centred focus in the classroom by providing a rich set of resources and strategies to support flexible learning options for students who are registered in UBC courses. These strategies include the processes and best practices for the development of media-rich learning materials, agile approaches to course delivery, and instructional design strategies to better scaffold self-based, peer-based, and open learning efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".