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Teamwork, often mediated by version control systems such as Git and Apache Subversion (SVN), is central to professional programming. As a consequence, many colleges are incorporating both collaboration and online development environments into their curricula even in introductory courses. In this research, we collected GitHub logs from two programming projects in two offerings of a CS2 Java programming course for computer science majors. Students worked in pairs for both projects (one optional, the other mandatory) in each year. We used the students’ GitHub history to classify the student teams into three groups, collaborative, cooperative, or solo-submit, based on the division of labor. We then calculated different metrics for students’ teamwork including the total number and the average number of commits in different parts of the projects and used these metrics to predict the students’ teamwork style. Our findings show that we can identify the students’ teamwork style automatically from their submission logs. This work helps us to better understand novices’ habits while using version control systems. These habits can identify the harmful working styles among them and might lead to the development of automatic scaffolds for teamwork and peer support in the future. 

Discussion forums are the primary channel for social interaction and knowledge sharing in Massive Open Online Courses (MOOCs). Many researchers have analyzed social connections on MOOC discussion forums. However, to the best of our knowledge, there is little research that distinguishes between the types of connections students make based upon the content of their forum posts. We analyze this effect by distinguishing on- and off-topic posts and comparing their respective social networks. We then analyze how these types of posts and their social connections can be used to predict the students’ final course performance. Pursuant to this work we developed a binary classifier to identify on- and off- topic posts and applied our analysis with the hand-coded and predicted labels. We conclude that the post type does affect the relationship between the students and their closest neighbors or community members clustered communities and their closest neighbor to their learning outcomes. 

Students’ interactions with online tools can provide us with insights into their study and work habits. Prior research has shown that these habits, even as simple as the number of actions or the time spent on online platforms can distinguish between the higher performing students and low-performers. These habits are also often used to predict students’ performance in classes. One key feature of these actions that is often overlooked is how and when the students transition between different online platforms. In this work, we study sequences of student transitions between online tools in blended courses and identify which habits make the most difference between the higher and lower performing groups. While our results showed that most of the time students focus on a single tool, we were able to find patterns in their transitions to differentiate high and low performing groups. These findings can help instructors to provide procedural guidance to the students, as well as to identify harmful habits and make timely interventions.