Title: Design of an online learning early warning system based on learning behaviour analysis

Authors: Xin Li; Tong Zhou

Addresses: School of Big Data and Internet of Things, Chongqing Vocational Institute of Engineering, No. 1, North-South Avenue, Jiangjin District, Chongqing, 402260, China ' School of Big Data and Internet of Things, Chongqing Vocational Institute of Engineering, No. 1, North-South Avenue, Jiangjin District, Chongqing, 402260, China

Abstract: The objective of this study is to design an academic warning system to identify students with abnormal learning behaviours, to give early warning of students' learning status, and to help students successfully complete their studies. In this study, the comprehensive academic performance of university students is taken as the research object, and the basic information data, academic performance data, and online data are collected. After analysing the students' learning behaviour, an online learning warning system is constructed based on grid based on clustering by fast search and find of density peaks (GBCFSFDP) algorithm, and the system is divided into dynamic warning and static warning. It found that the clustering of students' learning behaviour completed by GBCFSFDP algorithm can be divided into six specific categories, and the attributes that influence the learning performance can be differentiated by weighted naive Bayes classification algorithm based on fruit fly optimisation algorithm (WNBC-FOA). The results show that the time spent online have the greatest impact on students' performance. Therefore, this online learning early warning system can make it easier for students and teachers to understand the reasons for learning abnormalities and the correlation between different behaviours and academic performance, so as to make corresponding improvements.

Keywords: learning behaviour analysis; learning early warning system; GBCFSFDP algorithm; WNBC-FOA algorithm.

DOI: 10.1504/IJCEELL.2021.116035

International Journal of Continuing Engineering Education and Life-Long Learning, 2021 Vol.31 No.3, pp.381 - 393

Received: 26 Jul 2019
Accepted: 14 Jan 2020
Published online: 06 Jul 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article