Growth and maturity of intelligent tutoring systems: a status report
Smart machines in education
Evaluating tutors that listen: an overview of project LISTEN
Smart machines in education
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Using the cloze procedure to assess program reading comprehension
SIGSCE '84 Proceedings of the fifteenth SIGCSE technical symposium on Computer science education
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling and understanding students' off-task behavior in intelligent tutoring systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
Temporal Data Mining for Educational Applications
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Factors influencing the performance of Dynamic Decision Network for INQPRO
Computers & Education
Relating Machine Estimates of Students' Learning Goals to Learning Outcomes: A DBN Approach
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Does Learner Control Affect Learning?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Modeling learning patterns of students with a tutoring system using Hidden Markov Models
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Can a Computer Listen for Fluctuations in Reading Comprehension?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Learning Engagement: What Actions of Learners Could Best Predict It?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Classifying learner engagement through integration of multiple data sources
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Log file analysis for disengagement detection in e-Learning environments
User Modeling and User-Adapted Interaction
Off-Task Behavior in Narrative-Centered Learning Environments
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Learning to Identify Students' Off-Task Behavior in Intelligent Tutoring Systems
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
Modeling mental workload using EEG features for intelligent systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
When does disengagement correlate with learning in spoken dialog computer tutoring?
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Integrating learning and engagement in narrative-centered learning environments
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Detecting gaming the system in constraint-based tutors
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Gaze tutor: A gaze-reactive intelligent tutoring system
International Journal of Human-Computer Studies
Better student assessing by finding difficulty factors in a fully automated comprehension measure
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Adapting to when students game an intelligent tutoring system
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Automatic recognition of learner groups in exploratory learning environments
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Estimating student proficiency using an item response theory model
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
Integrating learning, problem solving, and engagement in narrative-centered learning environments
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Cross-system validation of engagement prediction from log files
EC-TEL'07 Proceedings of the Second European conference on Technology Enhanced Learning: creating new learning experiences on a global scale
User Modeling and User-Adapted Interaction
When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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Time on task is an important predictor for how much students learn. However, students must be focused on their learning for the time invested to be productive. Unfortunately, students do not always try their hardest to solve problems presented by computer tutors. This paper explores student disengagement and proposes an approach, engagement tracing, for detecting whether a student is engaged in answering questions. This model is based on item response theory, and uses as input the difficulty of the question, how long the student took to respond, and whether the response was correct. From these data, the model determines the probability a student was actively engaged in trying to answer the question. The model has a reliability of 0.95, and its estimate of student engagement correlates at 0.25 with student gains on external tests. We demonstrate that simultaneously modeling student proficiency in the domain enables us to better model student engagement. Our model is sensitive enough to detect variations in student engagement within a single tutoring session. The novel aspect of this work is that it requires only data normally collected by a computer tutor, and the affective model is statistically validated against student performance on an external measure.