Abstract
This paper aims at finding an efficient way for discovering which specific knowledge each student does not possess in order to successfully start a new course or to proceed with another section in a current subject. Most existing tutoring systems respond to students’ mistakes by providing links to a collection of teaching materials. Such an approach does the individual needs of each student. Our idea is to apply a holistic approach that involves looking at the whole system of each student knowledge within an subject rather than just concentrating on single mistakes, lack of knowledge or misconception.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD international conference on management of data, Washington, DC, USA, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: Proceedings of the 20th very large data base conference, Santiago, Chile, pp. 487–489 (1994)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Uthurusamy, F., Piatetsky-Shapiro, G., Smyth, P. (eds.) Advances in Knowledge discovery of association rules, pp. 307–328. MIT Press, Cambridge (1996)
Bastide, T., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining frequent patterns with counting inference. SIGKDD explorations, Special issue on scalable algorithms 2(2), 71–80 (2000)
Brin, S., Motwani, R., Ullmann, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGKDD international conference on management of data, Tuscon, AZ, USA, pp. 255–264 (1997)
Carpineto, C., Romano, G.: Concept Data Analysis: Theory and Applications. John Wiley and Sons, Ltd., Chichester (2004)
Delgado, M., Sanchez, D., Martin-Bautista, M.J., Vila, M.A.: Mining association rules with improved semantics in medical databases. Artificial Intelligence in Medicine 21(1-3), 241–250 (2001)
Ganter, B., Stumme, G., Wille, R. (eds.): Formal Concept Analysis. LNCS (LNAI), vol. 3626. Springer, Heidelberg (2005)
Liu, C., Zheng, L., Ji, J., Yang, C., Li, J., Yang, W.: Electronic homework on the WWW. In: First Asia- Pacific Conference on Web Intelligence, pp. 540–547 (2001)
Malerba, D., Lisi, F.A., Appice, A., Sblendorio, F.: Mining spatial association rules in census data: a relational approach. In: Proceedings of the ECML/PKDD 2002 workshop on mining official data. University Printing House, Helsinki, pp. 80–93 (2002)
Martin, J., Vanlehn, K.: Student assessment using Bayesian nets. International Journal of Human- Computer Studies 42, 575–591 (1995)
Merceron, A., Yacef, K.: A Web-based Tutoring Tool with Mining Facilities to Improve Learning and Teaching. In: Verdejo, F., Hoppe, U. (eds.) Proceedings of 11th International Conference on Artificial Intelligence in Education, Sydney. IOS Press, Amsterdam (2003)
Mueller, A.: Fast sequential and parallel algorithms for association rule mining: A comparison. Technical Report Technical Report CS-TR- 3515, University of Maryland, College Park, MD (1995)
Sweiger, M., Madsen, M.R., Langston, J., Howard Lombard, H.: Clickstream data warehousing. John Wiley & Sons, Chichester (2002)
Wille, R.: Concept lattices and conceptual knowledge systems. Computers Math. Applic. 23(6-9), 493–515 (1992)
Zaki, M.J.: Generating non-redundant association rules. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston, USA, pp. 34–43 (2000)
Zaki, M.J., Hsiao, C.-J.: CHARM: An efficient algorithm for closed itemset mining. In: Proceedings of the 2nd SIAM international conference on data mining, Arlington, VA, USA, pp. 34–43 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Encheva, S., Tumin, S. (2006). Application of Association Rules for Finding Correlations Among Students Preliminary Knowledge. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2006. Lecture Notes in Computer Science, vol 4101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11863649_37
Download citation
DOI: https://doi.org/10.1007/11863649_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44494-7
Online ISBN: 978-3-540-44496-1
eBook Packages: Computer ScienceComputer Science (R0)