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.
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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
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DOI: https://doi.org/10.1007/978-3-662-44188-6_28
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