Machine learning pedagogy to support the research community
Bibliographic record
Abstract
Machine learning methods are increasingly leveraged in disparate domains of research. Herein, we describe our curriculum design to introduce undergraduate students to applied research through a series of course assignments and a competition among peers to inspire other educators. We describe the overall course structure and detail how the assignments were tailored to a selected open research question while developing student understanding of machine learning. We outline the lessons learned from this new undergraduate curriculum design and describe how it may be adapted to similar courses. For the selected COVID19-related course-long problem of predicting which drugs might interact with specific proteins, we leveraged state-of-the-art tools for representing drug and protein sequences. We challenged students to develop unique solutions competitive with a current state-of-the-art model using reproducible Notebooks and cloud-based computing resources with the expectation that top-ranking solutions would be used to predict novel druggable targets within the SARS-CoV-2 proteome to possibly treat COVID19 patients. We motivate this curriculum design based on related competition frameworks that have led to notable research advancements and contributed to machine learning pedagogy. From our experience, the top student solutions were ultimately combined using a stacked classifier to create a publishable solution representing an actual research contribution. We highly recommend introducing undergraduate students to open research applications early in their program to encourage them to consider pursuing a career in research.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".