What Is Hard about Teaching Machine Learning to Non-Majors? Insights from Classifying Instructors’ Learning Goals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given its societal impacts and applications to numerous fields, machine learning (ML) is an important topic to understand for many students outside of computer science and statistics. However, machine-learning education research is nascent, and research on this subject for non-majors thus far has only focused on curricula and courseware. We interviewed 10 instructors of ML courses for non-majors, inquiring as to what their students find both easy and difficult about machine learning. While ML has a reputation for having algorithms that are difficult to understand, in practice our participating instructors reported that it was not the algorithms that were difficult to teach, but the higher-level design decisions. We found that the learning goals that participants described as hard to teach were consistent with higher levels of the Structure of Observed Learning Outcomes (SOLO) taxonomy, such as making design decisions and comparing/contrasting models. We also found that the learning goals that were described as easy to teach, such as following the steps of particular algorithms, were consistent with the lower levels of the SOLO taxonomy. Realizing that higher-SOLO learning goals are more difficult to teach is useful for informing course design, public outreach, and the design of educational tools for teaching ML.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it