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Objectives The traditional, instructor-led form of live coding has been extensively studied, with findings showing that this form of live coding imparts similar learning to static-code examples. However, a concern with Traditional Live Coding is that it can turn into a passive learning activity for students as they simply observe the instructor program. Therefore, this study compares Active Live Coding—a form of live coding that leverages in-class coding activities and peer discussion—to Traditional Live Coding on three outcomes: 1) students’ adherence to effective programming processes, 2) students’ performance on exams and in-lecture questions, and 3) students’ lecture experience. Participants Roughly 530 students were enrolled in an advanced, CS1 course taught in Java at a large, public university in North America. The students were primarily first- and second-year undergraduate students with some prior programming experience. The student population was spread across two lecture sections—348 students in the Active Live Coding (ALC) lecture and 185 students in the Traditional Live Coding (TLC) lecture. Study Methods We used a mixed-methods approach to answer our‘ research questions. To compare students’ programming processes, we applied process-oriented metrics related to incremental development and error frequencies. To measure students’ learning outcomes, we compared students’ performance on major course components and used pre- and post-lecture questionnaires to compare students’ learning gain during lectures. Finally, to understand students’ lecture experience, we used a classroom observation protocol to measure and compare students’ behavioral engagement during the two lectures. We also inductively coded open-ended survey questions to understand students’ perceptions of live coding. Findings We did not find a statistically significant effect of ALC on students’ programming processes or learning outcomes. It seems that both ALC and TLC impart similar programming processes and result in similar student learning. However, our findings related to students’ lecture experience shows a persistent engagement effect of ALC, where students’ behavioral engagement peaks andremains elevatedafter the in-class coding activity and peer discussion. Finally, we discuss the unique affordances and drawbacks of the lecture technique as well as students’ perceptions of ALC. Conclusions Despite being motivated by well-established learning theories, Active Live Coding did not result in improved student learning or programming processes. This study is preceded by several prior works that showed that Traditional Live Coding imparts similar student learning and programming skills as static-code examples. Though potential reasons for the lack of observed learning benefits are discussed in this work, multiple future analyses to further investigate Active Live Coding may help the community understand the impacts (or lack thereof) of the instructional technique. 

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.