Modeling and understanding students' off-task behavior in intelligent tutoring systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Affective and behavioral predictors of novice programmer achievement
ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Addressing the assessment challenge with an online system that tutors as it assesses
User Modeling and User-Adapted Interaction
The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate?
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
International Journal of Human-Computer Studies
Towards predicting future transfer of learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Exploring the relationship between novice programmer confusion and achievement
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Modeling learner affect with theoretically grounded dynamic bayesian networks
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Detecting learning moment-by-moment
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Clustering of design decisions in classroom visual displays
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Temporal learning analytics for computer based testing
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Hi-index | 0.00 |
In this paper, we investigate 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. The relationships between affect and learning outcomes have been previously studied, but not in a manner that is both longitudinal and finer-grained. Affect detectors are used to estimate student affective states 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 8th 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 frustration are both associated with positive learning outcomes, surprisingly in the case of frustration.