Title: An anomaly detection of learning behaviour data based on discrete Markov chain

Authors: Dahui Li; Peng Qu; Tao Jin; Changchun Chen; Yunfei Bai

Addresses: School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China ' School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China ' School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China ' School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China ' School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China

Abstract: In order to overcome the problems of large anomaly detection error and long detection time in traditional learning behaviour data anomaly detection methods, this paper proposes a learning behaviour data anomaly detection method based on discrete Markov chain. This method analyses the types of learning behaviour data, and determines the influencing factors of learning behaviour data. With the help of support vector machine, the data extraction range is determined, and the data redundancy is determined to complete the data pre-processing. This paper analyses the basic principle of discrete Markov chain, constructs the discrete Markov chain model, and completes the detection of abnormal learning behaviour data. The experimental results show that the maximum detection error of the proposed method is about 2%, and the detection time is always less than 2.5 s.

Keywords: discrete Markov chain; learning behaviour data; support vector machine; redundancy.

DOI: 10.1504/IJCEELL.2023.127872

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.1, pp.69 - 83

Received: 10 Dec 2020
Accepted: 23 Feb 2021
Published online: 20 Dec 2022 *