UC Riverside

Educational Data Mining is a nascent, but rapidly growing field which has typically applied data mining techniques to educational data extracted from digital systems such as Learning Content Management Systems and Intelligent Tutoring Systems.

The research thus far has been able to identify interesting patterns in the ways students learn when using these systems, but an analysis of students' ordinary problem-solving processes remains unexplored.

In this work we apply data mining and machine learning techniques to a digital data set of students' ordinary, handwritten coursework in the context of a Mechanical Engineering course. This work makes four major contributions. It is the first, to our knowledge, study in which data mining and machine learning techniques have been applied to students' problem-solving processes in their ordinary learning environment, using pen and paper at home or in the classroom; because the data set is unique, we provide an in-depth description of the large digital collection of students' handwritten course work we have collected. Second, we investigate novel, discrete and numerical representations of students' handwritten coursework which characterize different aspects of students' ordinary problem-solving processes. Third, we identify patterns in these representations as well as correlations between these representations and course performance using various machine learning and data mining techniques. Last and most importantly, we present interesting conclusions that may be drawn from these patterns and correlations, which allow instructors of the course which was investigated to improve future offerings.