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Nationally, more than one-third of students enrolling in introductory computer science programming courses (CS101) do not succeed. To improve student success rates, this research team used supervised machine learning to identify students who are "at-risk" of not succeeding in CS101 at a two-year public college. The resultant predictive model accurately identifies \(\approx\)99% of "at-risk" students in an out-of-sample test data set. The programming instructor piloted the use of the model'sdoi:10.21203/rs.3.rs-1025335/v1 fatcat:ki57uiayy5fqlokgemxj2n5oge