Towards Emotion-awareness in Programming Education with Behavior-based Emotion Estimation

Existing studies in both psychology and software engineering have shown the importance of emotions in complex learning and programming tasks. For students who are learning to program, rich emotions are experienced which can provide valuable feedback to their teachers. To accurately model stu-dents' emotions, this paper adopts the well-recognized model of emotions during complex learning that involves four states: engaged, confused, frustrated, and bored. To perform continuous estimation of students' emotions in a non-intrusive manner, this paper proposes to track students' programming behavior and estimate their corresponding emotional states. Compare to the existing approaches on acquiring the students' emotional states with self-reports or bio-sensors, the proposed approach is more feasible in conducting real-world, and large-scale studies for not requiring extensive human interventions or additional devices. Evaluated using data collected from a real-world course project, the proposed approach is showed to be promising for achieving an estimation accuracy of 72.06 % for the above four emotional states. As an enabling technology, the proposed is potentially useful in supporting many applications and improve the quality of programming education in computer science.