Proceedings of the Second (2015) ACM Conference on Learning @ Scale
Bibliographic record
Abstract
It is our great pleasure to welcome you to ACM conference Learning at Scale 2015. In this, the second year of the conference, we have seen a significant growth in the number of submissions to the conference and an overall improvement in the quality of the contributions. This year's conference continues the tradition of being the premier forum for presentation of research results and inside stories about what makes online educational systems operate at scale. The call for papers attracted submissions from all over the world, covering a broad range of topics from the theoretical to the pragmatic. The program committee reviewed and accepted the following: Venue or Track Reviewed Accepted Full Technical Papers 90 23 25% Short Technical Papers 12 5 41%Work in Progress Papers 54 47 80% Since the conference is still in its formative years, we accepted a large fraction of all the Works in Progress because we found the experience of reading through them to be so valuable. We are still a nascent field, and learning about the very latest work reflects the rapidly changing nature of what we know to be true. We encourage attendees to attend both keynotes. These valuable and insightful talks can and will guide us to a better understanding of the future of our field: Achieving 96% mastery at national scale through inspired learning and generative adaptivity, Zoran Popovic (University of Washington)Machine Learning for Learning at Scale, Peter Norvig (Google)
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.243 | 0.087 |
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".