Learning technology platforms today can be equipped with infrastructure to capture fine-granular behavioral data about students as they proceed through a course. This includes, for example, the sequence of clicks made while watching a video lecture, answering a quiz question, or posting in a discussion. The resultant “big” data presents unprecedented opportunities to study the process by which learning occurs.
Big Learning Data Analytics (BLDA) involves developing methodology to extract patterns from student behavioral data, and using the insights to design algorithms for learning analytics and individualization. It has a plethora of research questions at its core, such as: What is the most effective way to model the learning process, such that it leads to keen analytic insights? How can we identify the relationship between student engagement and performance? Are there hidden dimensions that dictate how users respond to different types of information?
One useful type of analytics is prediction, i.e., predicting student quiz performance or other types of learning outcomes. To investigate this, we have used millions of clickstream logs generated from tens of thousands of students that have taken our MOOC courses. In particular, we have developed different methods for representing the behavior students exhibit while watching lecture videos, and have used the insights in the design of algorithms to predict student performance on quiz questions in advance. These behavior-based algorithms have superior quality to more conventional collaborative filtering methods that rely solely on past quiz performance. They also obtain quality predictions early in courses, demonstrating an early-detection capability that is useful for instructors to identify struggling learners in advance.
For more information, feel free to email Christopher Brinton at cbrinton [at] princeton [dot] edu.