Title: Evaluation of learners' online learning behaviour based on the analytic hierarchy process

Authors: Xuejiao Huang; Xiaojin Wang; Fengjuan Liu

Addresses: School of Information Science and Technology, Northeast Normal University, Jilin, China ' School of Vocational Education Teachers, Guangdong Polytechnic Normal University, 293, West Zhongshan Dadao, Tianhe District, Guangzhou, 510665, China ' School of Educational Science, Shaanxi Sci-Tech University, Hanzhong Shaanxi 723000, China

Abstract: The online learning behaviour has become an important factor in predicting the learning achievement. Evaluating online learning behaviour is one of the hot topics in the field of IT education, but does not carry out specific weights and score distribution. Therefore, the online learning behaviour data from the 'Moso Teach' cloud platform was analysed by correlation analysis, cluster analysis and analytic hierarchy process analysis. We found that problem-solving behaviour of learners is the least, which is not common in online learning behaviour, while social interaction behaviour is better than problem-solving behaviour, and resource learning behaviour is the most common in online learning. The score distribution diagram of resource learning behaviour shows an inverse s-shaped curve, while the curves of the score distribution diagram of social interaction behaviour and problem solving behaviour are close to a straight line. There are learning achievement differences in resource learning behaviour and problem-solving behaviour, and core-marginal differences in resource learning behaviour and social interaction behaviour.

Keywords: online learning; learning behaviour; learning analysis.

DOI: 10.1504/IJCEELL.2021.115988

International Journal of Continuing Engineering Education and Life-Long Learning, 2021 Vol.31 No.3, pp.311 - 324

Received: 15 May 2019
Accepted: 03 Dec 2019

Published online: 27 Apr 2021 *

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