Adaptive feedback based on student emotion in a system for programming practice

Abstract

We developed a system for programming practice that provides adaptive feedback based on the presence of confusion on the student. The system provides two types of adaptive feedback. First, it can control the complexity of the exercises presented to the student. Second, it can offer guides for the exercises when needed. These feedback are based on the presence of confusion, which is detected based on the student’s compilations, typing activity, and facial expressions using a hidden Markov model trained on data collected from introductory programming course students. In this paper we discuss the system, the approach for detecting confusion, and the types of adaptive feedback displayed. We tested our system on Japanese university students and discuss the results and their feedback. This study can lay the foundation for the development of intelligent programming tutors that can generate personalized learning content based on the state of the individual learner.