Identifying affective trajectories in relation to learning gains during the interaction with a tutoring system

Abstract

This paper presents the identification of sequences of affective states and its consequential impact to learning in an intelligent tutor for Mathematics at secondary level. These trajectories are represented as time series obtained by DAE 1.0 a software capable of detecting and labeling points in human faces in relation to affective states. Data was collected from students (N=44) in one secondary school, in a semirural town in Veracruz, Mexico. The students were asked to interact with the tutoring system for 40 minutes and were photographed by DAE 1.0 at a pace of 1 picture each 5 seconds. Based on a dataset consisting of 480 pictures per student, we employed the SAX algorithm to make the data discrete and facilitate the interpretation of the time series. The results of classifying the data using ID3 showed an accuracy of 62.85% in identification of affective trajectories related to higher learning gains. Future studies will seek to test this algorithm on a different data set with the aim of predicting performance towards personalizing affective interventions in the tutoring system.