Investigating Automated Student Modeling in a Java MOOC

Abstract

With the advent of ubiquitous web, programming is no longer a sole prerogative of computer science schools. Scripting languages are taught to wider audiences and programming has become a flag post of any technology related program. As more and more students are exposed to coding, it is no longer a trade of the select few. As a result, students who would not opt for a coding class a decade ago are in a position of having to learn a rather difficult subject. The problem of assisting students in learning programming has been explored in several intelligent tutoring systems. The key component of such systems is a student model that keeps track of student progress. In turn, the foundation of a student model is a domain model – a vocabulary of skills (or concepts) that structures the representation of student knowledge. Building domain models for programming is known as a complicated task. In this paper we explore automated approaches for extracting domain models for learning programming languages and modeling student knowledge in the process of solving programming exercises. We evaluate the validity of this approach using large volume of student code submission data from a MOOC on introductory Java programming.