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In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected student’s help requests during coding assignments on two different platforms in a CS2 course, and categorized those requests into eight categories (including implementation, addressing test failures, general debugging, etc.). Then we analyzed the proportion of each type of requests and how they changed over time. We also collected student’s coding status (including what part of the code changed and the frequency of commits) before they seek help to investigate if students share a similar code change behavior leading to certain type of help requests. 

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.