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Recent research in computing has shown that student performance on prerequisite course content varies widely, even when students continue to progress further through the computing curriculum. Our work investigates instructors' perspectives on the purpose of prerequisite courses and whether that purpose is being fulfilled. In order to identify the range of instructor views, we interviewed twenty-one computer science instructors, at two institutions, that teach a variety of courses in their respective departments. We conducted a phenomenographic analysis on the interview transcripts, which revealed a wide variety of views on prerequisite courses. The responses shed light on various issues with prerequisite course knowledge, as well as issues around responsibility and conflicting pressures on instructors. These issues arise at the department level, as well as with individual course offerings. 

Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. The guesses come from knowledge gleaned from black box models. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize: when you are having trouble optimizing, just guess.