A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Constructivism in computer science education
SIGCSE '98 Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining and Knowledge Discovery
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Global Training of Document Processing Systems Using Graph Transformer Networks
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Adaptive teaching strategy for online learning
Proceedings of the 10th international conference on Intelligent user interfaces
Data Mining in E-learning (Advances in Management Information)
Data Mining in E-learning (Advances in Management Information)
The impact of learning styles on student grouping for collaborative learning: a case study
User Modeling and User-Adapted Interaction
Modeling student online learning using clustering
Proceedings of the 44th annual Southeast regional conference
Real users, real results: examining the limitations of learning styles within AEH
Proceedings of the eighteenth conference on Hypertext and hypermedia
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
International Journal of Artificial Intelligence in Education
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor
International Journal of Artificial Intelligence in Education
Clustering and Sequential Pattern Mining of Online Collaborative Learning Data
IEEE Transactions on Knowledge and Data Engineering
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Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Clustering learners according to their collaboration
CSCWD '09 Proceedings of the 2009 13th International Conference on Computer Supported Cooperative Work in Design
Classifying learner engagement through integration of multiple data sources
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
To Tutor or Not to Tutor: That is the Question
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
How Much Assistance Is Helpful to Students in Discovery Learning?
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Adaptive collaborative web-based courses
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Adaptive support for distributed collaboration
The adaptive web
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
User Modeling and User-Adapted Interaction
An analysis of students' gaming behaviors in an intelligent tutoring system: predictors and impacts
User Modeling and User-Adapted Interaction
Content-free collaborative learning modeling using data mining
User Modeling and User-Adapted Interaction
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
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Dynamically adapting training systems based on user interactions
Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation
Artificial Intelligence Review
Modeling sequences of user actions for statistical goal recognition
User Modeling and User-Adapted Interaction
Data mining for adding adaptive interventions to exploratory and open-ended environments
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Whispering interactions to the end user using rules
RuleML'12 Proceedings of the 6th international conference on Rules on the Web: research and applications
User Modeling and User-Adapted Interaction
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
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Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners' problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.