Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes


  • Zach A Pardos University of California, Berkeley
  • Ryan S.J.D Baker Columbia Teacher's College
  • Maria San Pedro Columbia Teacher's College
  • Sujith M Gowda Arizona State University
  • Supreeth M Gowda Worcester Polytechnic Institute



Learning analytics, affect, confusion, boredom, high stakes tests, tutoring, automated detectors, prediction, educational data mining


In this paper, we investigate the correspondence between student affect and behavioural 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 in a manner that is both longitudinal and fine-grained. Affect and behaviour detectors are used to estimate student affective states and behaviour 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, or frustration, and estimates of the probability that the student is exhibiting off-task or gaming behaviours. We used data from the ASSISTments math tutoring system and found 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 behaviour, 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.

Author Biographies

Zach A Pardos, University of California, Berkeley

Assistant Professor

Schools of Education and Information

Ryan S.J.D Baker, Columbia Teacher's College

Associate Professor

Department of Human Development

Maria San Pedro, Columbia Teacher's College

Ph.D. student

Sujith M Gowda, Arizona State University

Research Associate

School of Computing, Informatics and Decision Systems Engineering

Supreeth M Gowda, Worcester Polytechnic Institute

Ph.D. student

Department of Learning Sciences and Technology




How to Cite

Pardos, Z. A., Baker, R. S., San Pedro, M., Gowda, S. M., & Gowda, S. M. (2014). Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes. Journal of Learning Analytics, 1(1), 107-128.



Research Papers