Abstract
Data mining has recently drawn an increasing interest as an effective approach to generation of a concept map in an adaptive learning platform that provides students with personalized learning guidance. Although it has seen significant progresses, the data mining-based concept map generation needs to be further improved both in complexity and accuracy for wide acceptance in actual education services. This paper aims to improve the accuracy of concept map by considering both wrong-to-wrong and correct-to-correct relationships of questions, and by adopting more accurate formulas in calculation of relevance degrees between concepts. Through simulations using a set of concepts, questions, and student test records sampled from a practical courseware, we show that the proposed approach can generate a more accurate and robust concept map at an acceptable additional complexity.
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Notes
- 1.
The test data was generated by several students and questions in Liner Algebra & Geometry, and has been modified to make it suitable for the purpose of the paper. The difficulty level of concepts increases from C 1 to C 4 .
References
Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: 20th International Conference on Very Large Database, pp. 487–499 (1994)
Bai, S.M., Chen, S.M.: Automatically constructing concept maps based on fuzzy rules for adaptive learning system. Experts Syst. Appl. 35(3), 1408–1414 (2008)
Bai, S.M., Chen, S.M.: Evaluating students’ learning achievement using fuzzy membership functions and fuzzy rules. Experts Syst. Appl. 35(1), 41–49 (2008)
Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. Int. J. Artific. Intell. Educ. 13, 159–172 (2003)
Carchiolo, V.L., Malgeri, M.: Adaptive formative paths in a web-based learning environment. Educ. Technol. Soc. 5(4), 64–75 (2002)
Chen, S.M., Bai, S.M.: Using data mining techniques to automatically construct concept maps for adaptive learning systems. Expert Syst. Appl. 37, 4496–4503 (2010)
Huang, X., Yang, K., Lawrence, V.B.: Classification-based approach to concept map generation in adaptive learning. In: 15th IEEE International Conference on Advanced Learning Technologies, Hualien, Taiwan (2015)
Lee, C.H., Lee, G.G., Leu, Y.H.: Application of automatically constructed concept map of learning to conceptual diagnosis of e-learning. Expert Syst. Appl. 36(2), 1675–1684 (2009)
Liao, S.H., Chu, P.H., Hsiao, P.Y.: Data mining techniques and applications – A decade review from 2000 to 2011. Expert Syst. Appl. 39, 11303–11311 (2012)
Millcevic, A.K., Vesin, B., Ivanovic, M., Budimac, Z.: E-learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56, 885–899 (2011)
Novak, J.D.: Learning, creating, and using knowledge, concept maps as facilitative tools in schools and corporations. Lawrence Erlbaum and Associates, New Jersey (1998)
Rahman, M.A., Islam, M.Z.: A hybrid clustering technique combining a novel genetic algorithm with k-means. Knowl.-Based Syst. 71, 345–365 (2014)
Sarem, M.A., Bellafkih, M., Ramdeni, M.: An approach for mining concepts’ relationships based on historical assessment records. Adv. Control Eng. Inf. Sci. 15, 3245–3249 (2011)
Sowan, B., Dahal, K., Hossain, M.A., Zhang, L., Spencer, L.: Fuzzy joint points based clustering algorithms for large data sets. Expert Syst. Appl. 40, 6928–6937 (2013)
Tsai, C.-J., Tseng, S.S., Lin, C.-Y.: A two-phase fuzzy mining and learning algorithm for adaptive learning environment. In: Alexandrov, V.N., Dongarra, J., Juliano, B.A., Renner, R.S., Tan, C. (eds.) ICCS-ComputSci 2001. LNCS, vol. 2074, pp. 429–438. Springer, Heidelberg (2001)
Tseng, S., Sue, P., Su, J., Weng, J., Tsai, W.: A new approach for constructing the concept map. Comput. Educ. 49, 691–707 (2007)
Wu, X., Kumar, V.: The Top Ten Algorithms in Data Mining. Chapman and Hall Publisher, Boca Raton (2009)
Yang, J., Huang, Z.X., Gao, Y.X., Liu, H.T.: Dynamic learning style prediction method based on a pattern recognition technique. IEEE Trans. Learn. Technol. 7(2), 165–177 (2014)
Zaki, M.J., Wagner Jr., M.: Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, United Kingdom (2014)
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Huang, X., Yang, K., Lawrence, V.B. (2015). An Efficient Data Mining Approach to Concept Map Generation for Adaptive Learning. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_18
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DOI: https://doi.org/10.1007/978-3-319-20910-4_18
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