Title: The blending teaching effect evaluation of distance education under the background of MOOC

Authors: Yingyao Wang

Addresses: School of Marxism, Changchun College of Electronic Technology, Changchun, 130000, China

Abstract: Aiming at the problems of low evaluation accuracy and large time cost of current evaluation methods, a blending teaching effect evaluation method of distance education under the background of MOOC is designed. Firstly, it constructs the blending teaching effect evaluation system of distance education under the background of MOOC from three aspects: students, teachers and the design of distance classroom teaching mode. Then, the evaluation index data is processed into consistent data with the help of normalisation method. The Euclidean distance between different index data is calculated by k-nearest neighbour algorithm, and the index data with noise is marked and removed. Finally, the evaluation matrix is used to determine the weight of the evaluation index, and the index data is put into the random forest model to obtain the relevant evaluation results. Experimental results show that this method has the comprehensive advantages of high evaluation accuracy and low time cost.

Keywords: MOOC; distance education; blending teaching effect; assessment; index system; k-nearest neighbour algorithm; evaluation matrix.

DOI: 10.1504/IJCEELL.2024.137108

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.2/3, pp.204 - 215

Received: 23 Mar 2022
Accepted: 12 Jul 2022
Published online: 01 Mar 2024 *

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