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Learning strategy feedback system for students using data mining technology

, Institute for Higher Education Research and Practice, Osaka University, Japan ; , Interfaculty Initiative in Information Studies, The University of Tokyo, Japan ; , Graduate School of Comprehensive Human Sciences, University of Tsukuba, Japan ; , , Interfaculty Initiative in Information Studies, The University of Tokyo, Japan ; , , , Benesse Corporation, Japan

EdMedia + Innovate Learning, in Honolulu, HI, USA ISBN 978-1-880094-73-0 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC


ABSTRACT: Using data mining technology, we developed a feedback system of learning strategies based on models that were created from data obtained from high school students. Using this system, we evaluated it from the following perspectives: (a) model validity, (b) effectiveness of the metaphors built into the system, and (c) users’ subjective evaluations. Results showed the following. (a) The models were validated because the difference of academic ability estimated from the models was reflected in data obtained from a monitor test. (b) The signal metaphor, representing the achievement levels of learning strategies, was effective because the length of time spent to read explanations of learning strategies differed according to the signal color; the direct road metaphor, representing the order of learning strategies, was understood by nearly half of the students. (c) Some users reported that the system animations were too long, but the overall system evaluation was favorable.


Matsukawa, H., Kitamura, S., Nagamori, Y., Hisamatsu, S., Yamauchi, Y., Nakano, M., Kanamori, Y. & Miyashita, N. (2009). Learning strategy feedback system for students using data mining technology. In G. Siemens & C. Fulford (Eds.), Proceedings of ED-MEDIA 2009--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 4269-4278). Honolulu, HI, USA: Association for the Advancement of Computing in Education (AACE). Retrieved December 16, 2018 from .

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