Title: Identifying student behavioural states in business English listening classroom based on SSD algorithm

Authors: Xuewei Gao; Juan Xin

Addresses: School of Humanities and Foreign Languages, Xi'an Aeronautical Institute, Xi'an, Shannxi, China ' Department of CIB Credit, MUFG Bank Ltd., England, London, UK

Abstract: By analysing students' behavioural states, one can evaluate their level of participation, engagement and focus in learning activities. Therefore, this study designs a method for identifying student behaviour states in business English listening classes based on the SSD algorithm. The behaviour state features are input into the improved SSD algorithm model. Within the model, behavioural state features are gradually extracted through operations such as convolution and pooling, and the classification and recognition results of student behavioural states are output. In the experimental results, the Cohen's Kappa coefficient of the identification results obtained by this method can reach 0.974, and the global minimum and maximum values of the Matthews correlation coefficient are 0.80 and 0.95, indicating that the identification results of this method are effective.

Keywords: business English listening class; student behaviour status; state identification; SSD algorithm; image enhancement; feature extraction.

DOI: 10.1504/IJCAT.2023.138838

International Journal of Computer Applications in Technology, 2023 Vol.73 No.4, pp.288 - 296

Received: 30 Aug 2023
Accepted: 15 Dec 2023
Published online: 31 May 2024 *

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