Help seeking, learning and contingent tutoring
Computers & Education
A plug-in architecture for generating collaborative agent responses
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
Limitations of Student Control: Do Students Know When They Need Help?
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
International Journal of Artificial Intelligence in Education - "Caring for the Learner" in honour of John Self
Note-taking for self-explanation and problem solving
Human-Computer Interaction
What matters in help-seeking? A study of help effectiveness and learner-related factors
Computers in Human Behavior
Modeling students' metacognitive errors in two intelligent tutoring systems
UM'05 Proceedings of the 10th international conference on User Modeling
The Influence of External-Regulation on Student Generated Questions during Hypermedia Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Can Help Seeking Be Tutored? Searching for the Secret Sauce of Metacognitive Tutoring
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Promoting Metacognition in Immersive Cultural Learning Environments
Proceedings of the 13th International Conference on Human-Computer Interaction. Part IV: Interacting in Various Application Domains
Scaffolding effective help-seeking behaviour in mastery and performance oriented learners
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
CTRL: A research framework for providing adaptive collaborative learning support
User Modeling and User-Adapted Interaction
IIT'09 Proceedings of the 6th international conference on Innovations in information technology
A framework for narrative adaptation in interactive story-based learning environments
Proceedings of the Intelligent Narrative Technologies III Workshop
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
International Journal of Artificial Intelligence in Education
International Journal of Advanced Intelligence Paradigms
An analysis of students' gaming behaviors in an intelligent tutoring system: predictors and impacts
User Modeling and User-Adapted Interaction
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of Artificial Intelligence in Education
Towards predicting future transfer of learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Towards the prediction of user actions on exercises with hints based on survey results
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
Towards the creation of a data-driven programming tutor
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Behavior effect of hint selection penalties and availability in an intelligent tutoring system
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
Detecting the moment of learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Core aspects of affective metacognitive user models
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
Detecting learning moment-by-moment
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Towards automatically detecting whether student learning is shallow
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
A cross-cultural comparison of effective help-seeking behavior among students using an ITS for math
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
User Modeling and User-Adapted Interaction
Hint systems may negatively impact performance in educational games
Proceedings of the first ACM conference on Learning @ scale conference
Personal and Ubiquitous Computing
Hi-index | 0.00 |
The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only helps students acquire domain-specific skills, but also develop better general help-seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the meta-cognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective help-seeking behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' help-seeking behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on help seeking has a positive effect on students' learning outcomes.