We present results of an intervention integrating a tool promoting metacognitive study behaviors, CompassX, in a Data Structures and Algorithms (CS2) course. Metacognition---commonly referred to as ''thinking about thinking''---has been consistently linked to improved learning strategies and student achievement. However, no prior literature has practiced an intervention that addresses all three commonly-accepted phases of metacognition. Thus in this work, we share the key features of CompassXthat promote metacognitive study behaviors, how our users engaged with those features, and how continued practice of metacognition using those features is related to improved learning outcomes. Students used CompassX voluntarily and some students did not fully engage with all metacognition-based features. However, continued engagement with a metacognitive feature appears to be indicative of higher exam scores. This also implies the possibility of utilizing a metacognitive tool to improve the performance of student outcome modeling by collecting a new type of behavioral data.

Higher education is expanding in United States prisons, with a growing demand for STEM offerings. Academics from other disciplines have stressed the importance of culturally relevant pedagogy (CRP) in prison higher education, and computing in context has shown major benefits in CS1--- especially for women and nontraditional students. More work is needed to determine what contexts are relevant to incarcerated college students, and how to incorporate these into computing curricula. In this paper, we build on prior work on computing in context and culturally relevant techniques in computing. We analyze course data from a CS1 course taught in a college-in-prison program to answer the following research question: What contexts do incarcerated students in CS1 find relevant? We identify 24 topics pursued by students across 78 open-ended programming assignment submissions, the three most popular being business management, sports statistics, and physical health. These results offer insight into potential contexts that are meaningful to incarcerated college students to be incorporated into future computing curricula and interventions in prisons.

Background and Context. Live coding is a teaching method in which an instructor dynamically writes code in front of students in an effort to impart skills such as incremental development and debugging. By contrast, traditional, static-code examples typically involve an instructor annotating or explaining components of pre-written code. Despite recommendations to use live coding and a wealth of qualitative analyses that identify perceived learning benefits of it, there are a lack of empirical evaluations to confirm those learning benefits, especially with respect to students’ programming processes.

Live coding---a pedagogical technique in which an instructor plans, writes, and executes code in front of a class---is generally considered a best practice when teaching programming. However, only a few studies have evaluated the effect of live coding on student learning in a controlled experiment and most of the literature relating to live coding identifies students' perceived benefits of live-coding examples. In order to empirically evaluate the impact of live coding, we designed a controlled experiment in a CS1 course taught in Python at a large public university. In the two remote lecture sections for the course, one was taught using live-coding examples and the other was taught using static-code examples. Throughout the term, we collected code snapshots from students' programming assignments, students' grades, and the questions that they asked during the remote lectures. We then applied a set of process-oriented programming metrics to students' programming data to compare students' adherence to effective programming processes in the two learning groups and categorized each question asked in lectures following an open-coding approach. Our results revealed a general lack of difference between the two groups across programming processes, grades, and lecture questions asked. However, our experiment uncovered minimal effects in favor of the live-coding group indicating improved programming processes but lower performance on assignments and grades. Our results suggest an overall insignificant impact of the style of presenting code examples, though we reflect on the threats to validity in our study that should be addressed in future work.

Incremental development is the process of writing a small snippet of code and testing it before moving on. For students in introductory programming courses, the value of incremental development is especially higher as they may suffer from more syntax errors, lack the proficiency to address complicated bugs, and may be more prone to frustration when struggling to correct code. However, to evaluate the effectiveness of interventions that aim to teach programming processes such as incremental development, we need to develop measures to assess such processes. In this paper, we present a way to measure incremental development. By qualitatively analyzing 15 student coding interviews, we identified common behaviors in the programming process that relate to incremental development. We then leveraged a dataset of over 1000 development sessions -- about 52,000 code snapshots at compilation time -- to automatically detect the common behaviors identified in our qualitative analysis. Finally, we crafted a formal metric, called the "Measure of Incremental Development'' (MID), to quantify how effectively a student used incremental development during a programming session. The MID detects common non-incremental development patterns such as excessive debugging after large additions of code to automatically assess a sequence of snapshots. The MID aligns with human evaluations of incrementality with over 80% accuracy. Our metric enables new research directions and interventions focused on improving students' development practices.