Open Access

ARTICLE

Intelligent Student Mental Health Assessment Model on Learning Management System

Nasser Ali Aljarallah^1,2, Ashit Kumar Dutta^3,*, Majed Alsanea⁴, Abdul Rahaman Wahab Sait⁵

1 AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
2 Department of Business Administration, Majmaah University, AlMajmaah, 11952, Kingdom of Saudi Arabia
3 Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
4 Department of Computing, Arabeast Colleges, Riyadh, 11583, Kingdom of Saudi Arabia
5 Department of Archives and Communication, King Faisal University, Al Ahsa, Hofuf, 31982, Kingdom of Saudi Arabia

* Corresponding Author: Ashit Kumar Dutta. Email:

Computer Systems Science and Engineering 2023, 44(2), 1853-1868. https://doi.org/10.32604/csse.2023.028755

Received 16 February 2022; Accepted 23 March 2022; Issue published 15 June 2022

Abstract

A learning management system (LMS) is a software or web based application, commonly utilized for planning, designing, and assessing a particular learning procedure. Generally, the LMS offers a method of creating and delivering content to the instructor, monitoring students’ involvement, and validating their outcomes. Since mental health issues become common among studies in higher education globally, it is needed to properly determine it to improve mental stability. This article develops a new seven spot lady bird feature selection with optimal sparse autoencoder (SSLBFS-OSAE) model to assess students’ mental health on LMS. The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression, anxiety, and stress (DAS). The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features. In addition, OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization (CSO) based parameter tuning process. The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classification outcomes. For examining the improved classifier results of the SSLBFS-OSAE model, a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.

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Keywords

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