Abstract
The paper introduces an improvement to an Educational Data Mining approach, which refrains from explicit learner modeling along with a recent refinement and evaluation. The technology models students’ learning characteristics by considering real data instead of deriving their characteristics explicitly. It aims at mining course characteristics interdependencies of former students’ study traces and utilizing them to optimize curricula of current students based to their performance traits revealed in their educational history. The recent refinement aims at increasing the sensitivity of the Data Mining technology by amplifying the influence of data, which shows interdependencies between the students’ talents and weaknesses and weakening the inuence of data from students, who perform about the same way in most courses (usually, very good or very poor in most subjects). Finally, the paper shows a validation approach by comparing the students’ performance with the degree of similarity of their curriculum to the curriculum proposed by our technology.
This is a preview of subscription content, log in via an institution to check access.
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
R. M. Felder, L. K. Silverman: Learning and teaching styles in engineering education. Engineering Education, vol. 78, no. 7, pp. 674-681, 1988.
H. Gardner: Frames of Mind: The Theory of Multiple Intelligences. New York: Basic Books, 1993.
S. Graf, T.-C. Liu, Kinshuk: Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, vol. 26, no. 2, pp. 116-131, 2010.
R. Knauf, Y. Sakurai, K. Takada, S. Dohi: Personalized Curriculum Composition by Learner Pro_le Driven Data Mining. Proc. of the 2009 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2009), ISBN 978-1-4244-2794-9, San Antonio, TX, USA, pp. 2137-2142, 2009.
R. Knauf, Y. Sakurai, S. Tsuruta, K. Takada: Empirical Evaluation of a Data Mining Method for Success Chance Estimation of University Curricula. Proc. of the 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), IEEE Catalog Number CFP10SMC-CDR, ISBN 978-1-4244-6587-3, ISSN 1062-922X, pp. 1127-1133, 2010.
R. Knauf, Y. Sakurai, S. Tsuruta, K. P. Jantke: Modeling Didactic Knowledge by Storyboarding. Journal of Educational Computing Research, vol. 42, no. 4, ISSN: 0735-6331, Baywood Publishing Company Inc., pp. 355-383, 2010.
S. Tsuruta, R. Knauf, S. Dohi, T. Kawabe, Y. Sakurai: An Intelligent System for Modeling and Supporting Academic Educational Processes. In Aljeandro Pen~aAlaya (Ed.): Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends, KES-Springer Verlag Book Series, vol. 17, pp.469-496, 2013.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yamamoto, Y., Knauf, R., Miyazawa, Y., Tsuruta, S. (2015). Increasing the Sensitivity of a Personalized Educational Data Mining Method for Curriculum Composition. In: Chen, G., Kumar, V., Kinshuk, ., Huang, R., Kong, S. (eds) Emerging Issues in Smart Learning. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44188-6_28
Download citation
DOI: https://doi.org/10.1007/978-3-662-44188-6_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44187-9
Online ISBN: 978-3-662-44188-6
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)