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  1. Dorn, Brian ; Vahrenhold, Jan (Ed.)
    Background and Context Lopez and Lister first presented evidence for a skill hierarchy of code reading, tracing, and writing for introductory programming students. Further support for this hierarchy could help computer science educators sequence course content to best build student programming skill. Objective This study aims to replicate a slightly simplified hierarchy of skills in CS1 using a larger body of students (600+ vs. 38) in a non-major introductory Python course with computer-based exams. We also explore the validity of other possible hierarchies. Method We collected student score data on 4 kinds of exam questions. Structural equation modeling was used to derive the hierarchy for each exam. Findings We find multiple best-fitting structural models. The original hierarchy does not appear among the “best” candidates, but similar models do. We also determined that our methods provide us with correlations between skills and do not answer a more fundamental question: what is the ideal teaching order for these skills? Implications This modeling work is valuable for understanding the possible correlations between fundamental code-related skills. However, analyzing student performance on these skills at a moment in time is not sufficient to determine teaching order. We present possible study designs for exploring this moremore »actionable research question.« less
    Free, publicly-accessible full text available June 1, 2023
  2. Proctoring educational assessments (e.g., quizzes and exams) has a cost, be it in faculty (and/or course staff) time or in money to pay for proctoring services. Previous estimates of the utility of proctoring (generally by estimating the score advantage of taking an exam without proctoring) vary widely and have mostly been implemented using an across subjects experimental designs and sometimes with low statistical power. We investigated the score advantage of unproctored exams versus proctored exams using a within-subjects design for N = 510 students in an on-campus introductory programming course with 5 proctored exams and 4 unproctored exams. We found that students scored 3.32 percentage points higher on questions on unproctored exams than on proctored exams (p < 0.001). More interestingly, however, we discovered that this score advantage on unproctored exams grew steadily as the semester progressed, from around 0 percentage points at the start of semester to around 7 percentage points by the end. As the most obvious explanation for this advantage is cheating, we refer to this behavior as the student population "learning to cheat". The data suggests that both more individuals are cheating and the average benefit of cheating is increasing over the course of the semester.more »Furthermore, we observed that studying for unproctored exams decreased over the course of the semester while studying for proctored exams stayed constant. Lastly, we estimated the score advantage by question type and found that our long-form programming questions had the highest score advantage on unproctored exams, but there are multiple possible explanations for this finding.« less