Web-based educational systems collect tremendous amount of electronic data, ranging from simple histories of students’ interactions with the system to detailed traces about their reasoning. However, less attention has been given to handling the large quantities of data collected from the students’ interactions and extracting pedagogically useful information from it. Such systems give teachers and learning researchers access to an extensive source of electronic data about students’ learning, data which is currently under-exploited. Data mining techniques have the potential to remedy this situation. Data mining encompasses a range of techniques and algorithms for discovering interesting patterns hidden in large data sets such as association rules, classification, cluster analysis as well as statistical analysis and database query. In this project, the goals are:
- To identify, adapt or create new data mining methods that are suited for turning learners’ performance data into information of relevance to teachers, instructional designers, and learning researchers.
- To define how to “massage” the student data so that we can extract interesting patterns
- To automate/facilitate some of the algorithm selection and pre-processing features
- To find suitable ways to present the results to users
- To exploit the patterns found to improve adaptation of teaching systems
Proceedings of International Conference in Educational Data Mining, 2016.
Proceedings of International Conference in Educational Data Mining, 2016.
Data Mining in the Classroom: Discovering Groupsâ€™ Strategies at a Multi-tabletop Environment Inproceedings
International Conference on Educational Data mining, EDM 2013, pp. 121-128, 2013.
An automatic approach for mining patterns of collaboration around an interactive tabletop. Inproceedings
International Conference on Artificial Intelligence in Education, AIED 2013, pp. 101-110, 2013.
Open learner models to support reflection on brainstorming at interactive tabletops. Inproceedings
International Conference on Artificial Intelligence in Education, AIED 2013, pp. 683-686, 2013.
Designing OLMs for Reflection about Group Brainstorming at Interactive Tabletops Inproceedings
2nd Workshop on Intelligent Support for Learning in Groups at the 16th International Conference on Artificial Intelligent in Education, pp. 27-36, 2013.
Educational Interfaces, Software, and Technology 2012: 3rd Workshop on UI Technologies and Educational Pedagogy, Austin, Texas, 2012.
CHI '12: Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems (Extended Abstracts), pp. 1775–1780, ACM, New York, NY, USA, 2012, ISBN: 978-1-4503-1016-1.
Unpacking traces of collaboration from multimodal data of collaborative concept mapping at a tabletop Inproceedings
International Conference of the Learning Sciences: The Future of learning, ICLS 2012, pp. 241-245, 2012.
An Interactive Teacher's Dashboard for Monitoring Groups in a Multi-tabletop Learning Environment Inproceedings
Proceedings of Intelligent Tutoring Systems, ITS 2012, pp. 482-492, Springer, 2012.
ANALYSING KNOWLEDGE GENERATION AND ACQUISITION FROM INDIVIDUAL AND FACE-TO-FACE COLLABORATIVE CONCEPT MAPPING Inproceedings
Canas, A J; Novak, J D; Vanhear, J (Ed.): Concept Maps: Theory, Methodology, Technology Proc. of the Fifth Int. Conference on Concept Mapping, pp. 17-24, 2012.
Orchestrating a multi-tabletop classroom: from activity design to enactment and reflection Inproceedings
Proceedings of Interactive Tabletops and Surfaces, ITS 2012, pp. 119-128, ACM, 2012.
Puntambekar, Sadhana; Erkens, Gijsbert; Hmelo-Silver, Cindy (Ed.): 12 , pp. 161–185, Springer, 2011.
Visualisations for longitudinal participation, contribution and progress of a collaborative task at the tabletop Inproceedings
International Conference on Computer Supported Collaborative Learning, CSCL 2011, pp. 25-32, 2011.
Modelling symmetry of activity as an indicator of collocated group collaboration Inproceedings
19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011, pp. 207-218, 2011.
Modelling and identifying collaborative situations in a collocated multi-display groupware setting Inproceedings
International Conference on Artificial Intelligence in Education, AIED 2011, pp. 196-204, 2011.
Designing tabletop-based systems for user modelling of collaboration Inproceedings
In Adaptive Support for Team Collaboration (ASTC2011) Workshop - The 19th International Conference on User Modeling, Adaptation and Personalization,(UMAP2011), pp. 47-51, 2011.
ACM International Conference on Interactive Tabletops and Surfaces, ITS 2011, pp. 172–181, Kobe, Japan, 2011, ISBN: 978-1-4503-0871-7.
Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop Inproceedings
4th International Conference on Educational Data Mining, EDM2011, pp. 111-120, 2011.
Educational Data Mining to Support Group Work in Software Development Projects Book Chapter
C. Romero S. Ventura, Pechenizkiy Baker M R (Ed.): pp. 173-186, Taylor and Francis, 2010.
Data Mining for Generating Hints in a Python Tutor Inproceedings
Educational Data Mining conference proceedings, pp. 91-100, Pittsburgh, USA, 2010.
Process Mining to Support Studentsâ€™ Collaborative Writing (Best Student Paper Award) Inproceedings
Educational Data Mining conference proceedings, pp. 257-266, Pittsburgh, USA, 2010.
Collaborative Concept Mapping at the Tabletop Inproceedings
ACM International Conference on Interactive Tabletops and Surfaces, ITS 2010, pp. 207–210, 2010.
Analysis of Collaborative Writing Processes Using Hidden Markov Models and Semantic Heuristics Inproceedings
Submitted to Workshop on Semantic Aspects in Data Mining (SADM) at ICDM2010, pp. 543-548, 2010.
IEEE Transactions on Knowledge and Data Engineering, 21 (6), pp. 759-772, 2009, ISSN: 1041-4347.
RÃ¨gles d'Association et Analyse d'Interactions d'Apprenants : Mesures d'IntÃ©rÃªt Inproceedings
TICE conference, paris, France, 2008.
Proceedings of Educational Data Mining Conference, pp. 57-66, Montreal, Canada, 2008.
Proceedings of Educational Data Mining workshop, pp. 60-69, Marina del Rey, CA, USA, 2007.
Finding Top-n Emerging Sequences to Contrast Sequence Sets Technical Report
Department of Computing Science, University of Alberta Edmonton, AB, Canada, (07-03), 2007.
Revisiting interestingness of strong symmetric association rules in educational data Inproceedings
Proceedings of International Workshop on Applying Data Mining in e-Learning (ADML'07), 2007.
Usage Analysis in Learning Systems: Existing Approaches and Scientific Issues Book
Mining patterns of events in students' teamwork data Inproceedings
Online Proceedings of the ITS (Intelligent Tutoring Systems) 2006 Workshop on Educational Data Mining, pp. 45-52, Jhongli, Taiwan, 2006.
Scrutable adaptation: because we can and must Inproceedings
Wade, V; Ashman, H; B.Smyth, (Ed.): Proceedings of Adaptive Hypermedia and Adaptive Web-Based Systems, 4th International Conference, AH2006, pp. 11-19, Springer, Dublin, Ireland, 2006.
Jhongli, Taiwan., 2006.
A Sequence Based Recommender System for Learning Resources Journal Article
Australian Journal of Intelligent Information Processing Systems, 9 , pp. 49–56, 2006.
A Sequence Based Recommender System for Learning Resources Inproceedings
Spink, Amanda ; Wilkinson, Ross (Ed.): 11th Australasian Document Computing Symposium (ADCS'06), Brisbane, Australia, 2006.
Proceedings of Usage Analysis in Learning Systems workshop, held in conjunction with AIED 2005 Proceeding
Amsterdam, The Netherlands, 2005.
Clustering students to help evaluate learning Book Chapter
Courtiat, Jean-Pierre; Davarakis, Costas; Villemur, Thierry (Ed.): 171 , pp. 31–42, Springer, 2005.
TADA-Ed for Educational Data Mining Journal Article
Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 7 (1), 2005.
Educational Data Mining: a Case Study Inproceedings
Looi, C K; McCalla, G; Bredeweg, B; Breuker, J (Ed.): Proceedings of the 12th Conference on Artificial Intelligence in Education, pp. 467–474, IOS Press, Amsterdam, The Netherlands, 2005.
Clustering students to help evaluate learning, Technology Enhanced Learning Book Chapter
Courtiat, Jean-Pierre ; Davarakis, Costas ; Villemur, Thierry (Ed.): 171 , pp. 31–42, Springer, 2005.
Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results Journal Article
Journal of Interactive Learning Research (JILR), 15 (4), pp. 319–346, 2004.
Train, store, analyse for more adaptive teaching Inproceedings
Proceedings of International Symposium Information and Knowledge Technologies in Higher Education and Industry (TICE2004), pp. 52–59, Technical University of Compigne, Compiegne, France, 2004.