Open Access

ARTICLE

Brain Storm Optimization Based Clustering for Learning Behavior Analysis

Yu Xue^1,2,*, Jiafeng Qin¹, Shoubao Su², Adam Slowik³

1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing, 211169, China
3 Department of Electronics and Computer Science, Koszalin University of Technology, 75-453, Koszalin, Poland

* Corresponding Author: Yu Xue. Email:

Computer Systems Science and Engineering 2021, 39(2), 211-219. https://doi.org/10.32604/csse.2021.016693

Received 08 January 2021; Accepted 28 April 2021; Issue published 20 July 2021

Abstract

Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students’ learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data. This study proposes a clustering algorithm based on brain storm optimization (CBSO) to categorize students according to their learning behaviors and determine their characteristics. This enables teaching to be tailored to taken into account those results, thereby, improving the education quality over time. Specifically, we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence. The experiments are performed on the 104 students’ online learning data, and the results show that CBSO is feasible and efficient.

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