In Unity is Strength - A story of global collaboration by edX Partners
Bibliographic record
Abstract
This series of 20 slides will be mainly imagery and relevant info-graphics charting the principles, objectives and achievements of the Microsoft led Assessment Symposium. Featuring narrative examples of how this band of online educators utilised the value of a level playing field, shared technical limitations of our common platform (edX) and a desire to work together to improve user experience and share best practice in student centred design. Between March 2017 and present day, this group has physically met 4 times (in Seattle, Adelaide, Vancouver and San Francisco) and has worked remotely together in between these sessions, forming a fruitful and positive community of practice with a safe-to-fail ethos. Evidence will be shared within this presentation documenting how Adelaide have leveraged the combined knowledge and experience of this group to access and beta test new learning tools (a Harvard designed chatbot), implement and share research into learner behaviour, cross promote MOOC courses from partner institutes to learners within the same target market and forge new and rich working groups to explore improvements to the technical side of the platform. In addition to the Microsoft Symposium group, the Regional edX Partnership will be detailed as a comparison, drawing on the difference in dynamic when consortium members are well known to each other geographically and compete within the same domestic market for the same (on campus and international)students. Discussion will focus on the strengths and opportunities that come with sharing experience locally with a smaller and more culturally aligned group versus the scope and depth of knowledge exchange with a truly global agenda.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.002 | 0.047 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".