Affect and behaviour detectors were used to estimate student affective states and behaviour based on analysis of tutor log data. For every student action, the detectors estimated the probability that the student was in a state of boredom, engaged concentration, confusion, or frustration. They also estimated the probability that the student was engaging in off‐task or gaming behaviours.
Boredom during problem solving was negatively correlated with performance
Boredom during scaffolded tutoring was positively correlated with performance
Confusion during problem solving was negatively correlated with performance
Confusion during scaffolded tutoring was positively correlated with performance
Engaged concentration was associated with positive learning outcomes
Frustration was associated with positive learning outcomes
Gaming the system was associated with poorer learning,
A unified model was used to predict student standardized examination scores from a combination of student affect, disengaged behaviour, and performance within the learning system. This model achieved high overall correlation with standardized exam scores.
Building on this work could produce positive evidence for ‘Proposition A: learning analytics improve learning outcomes’ and ‘Proposition B: Learning analytics improve learning support and teaching’.
This paper investigates the correspondence between student affect 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. Affect detectors were used to estimate student affective states based on post-hoc analysis of tutor log-data. For every student action in the tutor, the detectors provided an estimated probability that the student was in a state of boredom, engaged concentration, confusion, and frustration, and estimated the probability that they were exhibiting off-task or gaming behaviours. Boredom during problem solving was found to be negatively correlated with performance, however, boredom was positively correlated with performance when exhibited during scaffolded tutoring. A similar pattern was seen for confusion. Engaged concentration and, surprisingly, frustration were both associated with positive learning outcomes.
Comment from Martyn Cooper: This paper reports an investigation of the relationship between affective states of students as noted in online tutor logs and the performance in summative high stakes exams. An interesting result was that students who were bored or confused while answering the main problems, tended to do poorly on the test; however, boredom and confusion on scaffolding were associated with positive performance on the test. It is a good example of using analytics to assess students’ attitudes while learning. However, it is just a study with a single cohort over one year in a single limited learning environment used in maths education. The intention is to work towards better support for tutors to intervene appropriately given the students’ affective states and their contexts.
Citation: Z.A. Pardos, R. S. J. D. Baker, M. O. C. Z. San Pedro, S. M. Gowda, S. M. Gowda, (2013). Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes, In: Third International Learning Analytics and Knowledge Conference (LAK13), 8-12 April, Leuven, Belgium. | Url: http://dl.acm.org/citation.cfm?id=2460296