Student reflection is thought to be an important part of retaining and understanding knowledge gained in a course. Using natural language processing, we analyze and interpret student reflections from Massive Open Online Courses (MOOCs) to understand the students' sentiments and problem-solving procedures. The reflections are free text responses to questions from MIT 6.00.1x, an introductory programming MOOC. We compare different sentiment analysis methods, and conclude that the best-performing methods can robustly classify sentiment of student responses. In addition, we develop methods to analyze student problem-solving procedures using sentence parsing and topic modeling. We find our method can distinguish some common problem-solving procedures such as utilizing course resources.

We investigate student learning behaviors in a Massive Open Online Course with in-person components. Our goal is to improve the design of the course through learning analytics. The programming language taught, App Inventor, is a drag-and-drop language to create Android applications. We visualize and quantify student behaviors such as automatic and manual saving of code, video sections viewed, and the various forms of knowledge required to understand the course material. It appears students are less likely to go from course material that teaches procedures to other material that teaches procedures than we would expect, and rarely review previous topics covered in the course. We also find students tend to save marginally less at the beginning and end of sessions. However, since the data set is small, our conclusions are limited.