EDM
Kalina Yacef, Irena Koprinska, Judy Kay, Agathe Merceron
School of Information Technologies, University of Sydney
Contact Person
Kalina Yacef
kalina@it.usyd.edu.au
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Project Description
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
Key Publications
D. Perera, J. Kay, K. Yacef, I. Koprinska, and O. Zaiane. Clustering and Sequential Pattern Mining of Online Collaborative Learning Data, page to appear. IEEE Transactions on Knowledge and Data Engineering. 2008. [View Details]
A. Merceron and K. Yacef. Interestingness measures for association rules in educational data. In Proceedings of Educational Data Mining Conference, pages 57-66, 2008. [View Details]
D. Perera, J. Kay, K. Yacef, and I. Koprinska. Mining learners' traces from an online collaboration tool. In Proceedings of Educational Data Mining workshop, pages 60-69, 2007. [View Details]
R. O. Zaiane, K. Yacef, and J. Kay. Finding top-n emerging sequences to contrast sequence sets. Technical Report 07-03, Department of Computing Science, University of Alberta, Edmonton, AB, Canada, 2007. [View Details]
A. Merceron and K. Yacef. Revisiting interestingness of strong symmetric association rules in educational data. In Proceedings of International Workshop on Applying Data Mining in e-Learning (ADML'07), 2007. [View Details]
C. Choquet, V. Luengo, and K. Yacef. Usage Analysis in Learning Systems: Existing Approaches and Scientific Issues, volume 18 of Journal of Interactive Learning Research (JILR). 2007. [View Details]
J. Kay, N. Maisonneuve, K. Yacef, and O. Zaiane. Mining patterns of events in students' teamwork data. In Online Proceedings of the ITS (Intelligent Tutoring Systems) 2006 Workshop on Educational Data Mining, pages 45-52, Jhongli, Taiwan, 2006. [View Details]
J. Kay. Scrutable adaptation: because we can and must. In V. Wade, H. Ashman, and B.Smyth, editors, Proceedings of Adaptive Hypermedia and Adaptive Web-Based Systems, 4th International Conference, AH2006, pages 11-19. Springer, 2006. [View Details]
C. Heiner, R. Baker, and K. Yacef, editors. Proceedings of Educational Data Mining workshop, held in conjunction with the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan., 2006. [View Details]
D. Cummins, K. Yacef, and I. Koprinska. A sequence based recommender system for learning resources. Australian Journal of Intelligent Information Processing Systems, 9:49-56, 2006. [View Details]
D. Cummins, K. Yacef, and I. Koprinska. A sequence based recommender system for learning resources. In A. Spink and R. Wilkinson, editors, 11th Australasian Document Computing Symposium (ADCS'06), Brisbane, Australia, 2006. [View Details]
A. Merceron and K. Yacef. Clustering students to help evaluate learning, volume 171 of Technology Enhanced Learning, pages 31-42. Springer, 2005. [View Details]
A. Merceron and K. Yacef. Tada-ed for educational data mining. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 7(1), 2005. [View Details]
A. Merceron and K. Yacef. Educational data mining: a case study. In C. K. Looi, G. McCalla, B. Bredeweg, and J. Breuker, editors, Proceedings of the 12th Conference on Artificial Intelligence in Education, pages 467-474, Amsterdam, The Netherlands, 2005. IOS Press. [View Details]
A. Merceron and K. Yacef. Clustering students to help evaluate learning, Technology Enhanced Learning, volume 171, pages 31-42. Springer, 2005. [View Details]
C. Choquet, V. Luengo, and K. Yacef, editors. Proceedings of Usage Analysis in Learning Systems workshop, held in conjunction with AIED 2005, Amsterdam, The Netherlands, July 2005. [View Details]
A. Merceron and K. Yacef. Mining student data captured from a web-based tutoring tool: Initial exploration and results. Journal of Interactive Learning Research (JILR), 15(4):319-346, 2004. [View Details]
A. Merceron and K. Yacef. Train, store, analyse for more adaptive teaching. In Proceedings of International Symposium Information and Knowledge Technologies in Higher Education and Industry (TICE2004), pages 52-59, Compiegne, France, 2004. Technical University of Compigne. [View Details]
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