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As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated and lightweight means to identify students who are at risk of performing poorly in a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind. This study describes a modeling methodology that predicts student final exam scores in the third week of the term by using the clicker data that is automatically collected for instructors when they employ the Peer Instruction pedagogy. The modeling technique uses a support vector machine binary classifier, trained on one term of a course, to predict outcomes in the subsequent term. We applied this modeling technique to five different courses across the computer science curriculum, taught by three different instructors at two different institutions. Our modeling approach includes a set of strengths not seen wholesale in prior work, while maintaining competitive levels of accuracy with that work. These strengths include using a lightweight source of student data, affording early detection of struggling students, and predicting outcomes across terms in a natural setting (different final exams, minor changes to course content), across multiple courses in a curriculum, and across multiple institutions.

Beginning in 2008, we introduced a new CS1 incorporating a trio of best practices intended to improve the quality of the course, appeal to a broader student body, and, hopefully, improve retention in the major. This trio included Media Computation, Pair Programming, and Peer Instruction. After 3 and 1/2 years (8 CS1 classes, 3 different instructors, and 1011 students passing the course) we find that 89% of the majors who pass the course are still studying computing one year later. This is an improvement of 18% over our average retention of 71% for the previous version of the course (measured since Fall 2001). If the focus shifts from retention of passing CS1 majors to retention of CS1 initially enrolled majors, multiple improvements-fewer students drop, more students pass, and more passing students are retainedcompound to increase retention by 31% (from 51% to 82%). In this paper we analyze further aspects of these results, detail the three instructional design choices, and consider how they impact issues known to affect retention.

How pair programming, peer instruction, and media computation have improved computer science education.

Recent research suggests that the first weeks of a CS1 course have a strong influence on end-of-course student performance. The present work aims to refine the understanding of this phenomenon by using in-class clicker questions as a source of student performance. Clicker questions generate per-lecture and per-question data with which to assess student understanding. This work demonstrates that clicker question performance early in the term predicts student outcomes at the end of the term. The predictive nature of these questions applies to code-writing questions, multiple choice questions, and the final exam as a whole. The most predictive clicker questions are identified and the relationships between these questions and final exam performance are examined.

The Impostor Phenomenon (IP) is often discussed as a problem in the field of computer science, but there has yet to be an empirical study to establish its prevalence among CS students. One survey by the Blind app found that a high number of software engineers at some of the largest technology companies self-reported feelings of Impostor Syndrome; however, self-reporting of Impostor Syndrome is not the standard diagnostic for identifying whether an individual exhibits feelings of the Impostor Phenomenon. In this work, the established Clance IP Scale is used to identify the prevalence of IP among graduate and undergraduate computer science students at a large research-intensive North American institution. Among this population of over 200 students, 57% were found to exhibit frequent feelings of the Impostor Phenomenon with a larger fraction of women (71%) experiencing frequent feelings of the Imposter Phenomenon than men (52%). Additionally, IP was found to have greater prevalence among computer science students than among students of other populations from comparable studies. Due to the negative impacts associated with feelings of the Impostor Phenomenon, computer science education should work to improve student awareness and help student cope with these feelings. CCS CONCEPTS • Social and professional topics → Computing Education.

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Peer Instruction (PI) is an active learning pedagogical technique. PI lectures present students with a series of multiple-choice questions, which they respond to both individually and in groups. PI has been widely successful in the physical sciences and, recently, has been successfully adopted by computer science instructors in lower-division, introductory courses. In this work, we challenge readers to consider PI for their upper-division courses as well. We present a PI curriculum for two upper-division computer science courses: Computer Architecture and Theory of Computation. These courses exemplify several perceived challenges to the adoption of PI in upper-division courses, including: exploration of abstract ideas, development of high-level judgment of engineering design trade-offs, and exercising advanced mathematical sophistication. This work includes selected course materials illustrating how these challenges are overcome, learning gains results comparing these upper-division courses with previous lower-division results in the literature, student attitudinal survey results (N = 501), and pragmatic advice to prospective developers and adopters. We present three main findings. First, we find that these upper-division courses achieved student learning gains equivalent to those reported in successful lower-division computing courses. Second, we find that student feedback for each class was overwhelmingly positive, with 88% of students recommending PI for use in other computer science classes. Third, we find that instructors adopting the materials introduced here were able to replicate the outcomes of the instructors who developed the materials in terms of student learning gains and student feedback.