Affective states and state tests: Investigating how affect and engagement during the school year predict end of year learning outcomes

In this paper, we investigate the correspondence between student affect and behavioral engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year, on a high-stakes mathematics exam. The relationships between affect, behavioral engagement, and learning outcomes have been previously studied, but not in a manner that is both longitudinal and finer-grained. Affect and behavior detectors are used to estimate student affective states and behavior based on post-hoc analysis of tutor log-data. For every student action in the tutor the detectors give us an estimated probability that the student is in a state of boredom, engaged concentration, confusion, and frustration, and estimates of the probability that they are exhibiting off-task or gaming behaviors. We ran the detectors on two years of log data from 8 grade student use of the ASSISTments math tutoring system and collected corresponding end-of-year, high-stakes, state math test scores for the 1,393 students in our cohort. By correlating these data sources, we find that boredom during problem solving is negatively correlated with performance, as expected; however, boredom is positively correlated with performance when exhibited during scaffolded tutoring. A similar pattern is unexpectedly seen for confusion. Engaged concentration and, surprisingly, frustration are both associated with positive learning outcomes. In a second analysis we build a unified model that predicts student standardized examination scores from a combination of student affect, disengaged behavior, and performance within the learning system. This model achieves high overall correlation to standardized exam score, showing that these types of features can effectively infer longer-term learning outcomes.