Abstract
In recent years, distance education is rising gradually, and it is difficult to evaluate its teaching effect. In this context, based on the data mining of student behavior, a method of evaluating the effect of distance learning is designed. Implement data cleaning, data integration, data reduction and data transformation of teaching data of a distance learning platform. K-means algorithm based on Canopy and maximum minimum distance is designed to implement data mining of student behavior. On this basis, according to the three basic principles of “comprehensiveness”, “objectivity” and “learning-oriented”, through the analysis of data, the corresponding evaluation index is designed, so as to establish a set of effective evaluation system. Through testing, the evaluation method proposed in this paper is nearly 95% correct, and the F score is very low.
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Liu, Z., Gao, Q. (2024). Evaluation Method of Distance Teaching Effect Based on Student Behavior Data Mining. In: Gui, G., Li, Y., Lin, Y. (eds) e-Learning, e-Education, and Online Training. eLEOT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-51503-3_15
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DOI: https://doi.org/10.1007/978-3-031-51503-3_15
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