Can You Teach Me To Machine Learn?
Bibliographic record
Abstract
Machine learning (ML) has become an important topic for students across disciplines to understand because of its useful applications and its societal impacts. At the same time, there is little existing work on ML education, particularly about teaching ML to non-majors. This paper presents an exploration of the pedagogical content knowledge (PCK) for teaching ML to non-majors. Through ten interviews with instructors of ML courses for non-majors, we inquired about student preconceptions as well as what students find easy or difficult about learning ML. We identified PCK in the form of three preconceptions and five barriers faced by students, and six pedagogical tactics adopted by instructors. The preconceptions were found to concern themselves more with ML's reputation rather than its inner workings. Student barriers included underestimating human decision in ML and conflating human thinking with computer processing. Pedagogical tactics for teaching ML included strategically choosing datasets, walking through problems by hand, and customizing to the domain(s) of students. As we consider the lessons from these findings, we hope that this will serve as a first step toward improving the teaching of ML to non-majors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".