Intelligent Student Mental Health Assessment Model on Learning Management System Nasser Ali Aljarallah 1,2 , Ashit Kumar Dutta 3,* , Majed Alsanea 4 and Abdul Rahaman Wahab Sait 5 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: adotta@mcst.edu.sa Received: 16 February 2022; Accepted: 23 March 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 stabi- lity. This article develops a new seven spot lady bird feature selection with opti- mal 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 classi fication 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 classifica- tion 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. Keywords: Learning management system; mental health assessment; intelligent models; machine learning; feature selection; performance assessment 1 Introduction A learning management technique (LMS) is software for delivering, creating, and managing e-learning content. The organization uses LMS and interrelated software to deal with its online learning program. This earlier LMS which includes Blackboard and Moodle is simplification tool to organize instructor-led online This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computer Systems Science & Engineering DOI: 10.32604/csse.2023.028755 Article ech T Press Science