Here in Seattle, where I used to live, attending SIGCSE 2017.
Exposed! CS Faculty Caught Lecturing in Public: A Survey of Instructional Practices - Postsecondary Instructional Practices Survey (24 items), 7000 CS faculty invited, about 800 responses. If the evidence is clear that active-learning is better for instruction, then we should be doing that more. The overall split for CS was equal between student-centered and instructor-centered (exactly same avearge, 61.5). The survey showed clear differences between non-STEM (student) and STEM (instructor). So CS is doing better than its overall group.
Now, to dig into which differences there are in the demographics. The major difference in instructors is women, and those with 15 years of experience versus 30, both showing a 5+ point difference between student and instructor centered. However, 60s are still "whatever" and are not strongly committed. For those who are strongly committed, there are about 20% for each, while the remaining 60% are whatevers.
Investigating Student Plagiarism Patterns and Correlations to Grades - What are some of the patterns of the plagiarism, such as parts or all and how do students try to obfuscate their "work". Data from 2400 students taking a sophomore-level data structure course. After discarding those assignments with insufficient solution space, four assignments remained from six semesters. Used a plagiarism detector, to find likely cases of cheating.
First, even though the assignments remained unchanged, the rate of cases stayed constant. Most cases involved work from prior semesters. About two thirds of students who cheated, did so on only one assignment. Second, the rate of cheating on the individual assignments was similar to the partner assignment. Third, while students who cheated did better on those assignments, but they did not receive perfect scores and that those cheating did worse in the course than those who did not. And that those who took the follow-on course showed a larger grade difference (p=0.00019). Fourth, the analysis used the raw gradebook data that is independent of the detection and result of that detection.
Six detectors used. Lazy detector (common-case, no comments or whitespace), Token-based (all names become generic, sort functions by token length): identical token stream, modified token edit distance, and inverted token index (compute 12-grams and inversely weight how common these are). "Weird variable name" (lowercase, removed underscores). Obfuscation detector (all on one line, long variable names, etc). Fraction of total cases found by each detector: 15.69%, 18.49%, 49.71%, 72.77%, 67.35%, 0.38%.
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