Title: Prediction method of MOOC teaching effect based on data mining

Authors: Aiping Wang

Addresses: Department of Art, Puyang Vocational and Technical College, Puyang, 457000, China

Abstract: Aiming at the problems of low prediction accuracy and large prediction time of MOOC teaching effect, a method of MOOC teaching effect prediction based on data mining is proposed. Firstly, this paper analyses the learning behaviour of the course, obtains the learning characteristics, determines the type of learning behaviour data, completes the learning behaviour analysis based on the clustering algorithm, and finds all the learning behaviour data within the limited range. Then, by analysing the characteristics of MOOC teaching mode, we determine the teaching effect prediction index and build the effect prediction index system. Finally, based on the integrated learning algorithm, a multi-classifier is constructed to calculate the weight of the prediction index, and the MOOC teaching effect prediction model is constructed to complete the final prediction of the teaching effect. The test results show that the prediction accuracy of the proposed method is higher than 95%, and the maximum prediction time cost is 4 s, which can effectively improve the prediction accuracy, shorten the time cost, and have a good prediction effect.

Keywords: data mining; MOOC teaching effect; course characteristics; integrated learning; constraint function.

DOI: 10.1504/IJBIDM.2024.137736

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.3/4, pp.293 - 308

Received: 22 Nov 2022
Accepted: 07 Mar 2023
Published online: 04 Apr 2024 *

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