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large, that endeavors to convey and reinforce computing's social relevance and potential for positive societal impact. Besides the obvious benefit to society, CSG-Ed endeavors to exploit the finding that students' desire to have a positive societal impact is a strong determinant regarding their selection of a major[14]. A side effect of incorporating CSG-Ed activities, particularly in the introductory curriculum, is that it could potentially broaden participation in computing. It is worth noting that this "positive societal impact" is considered an inclusive term: CSG-Ed therefore includes sustainability [57], ICT4D [42], ICT4Peace [44], HFOSS [84], value sensitive design [61] and so on.

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.

Although it has never been rigourously demonstrated, there is a common belief that CS grades are bimodal. We statistically analyzed 778 distributions of final course grades from a large research university, and found only 5.8% of the distributions passed tests of multimodality. We then devised a psychology experiment to understand why CS educators believe their grades to be bimodal. We showed 53 CS professors a series of histograms displaying ambiguous distributions and asked them to categorize the distributions. A random half of participants were primed to think about the fact that CS grades are commonly thought to be bimodal; these participants were more likely to label ambiguous distributions as "bimodal". Participants were also more likely to label distributions as bimodal if they believed that some students are innately predisposed to do better at CS. These results suggest that bimodal grades are instructional folklore in CS, caused by confirmation bias and instructor beliefs about their students.

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