An Intelligent Tool Based on Fuzzy Logic and a 3D Virtual Learning Environment for Disabled Student Academic Performance Assessment

Abstract

In a virtual learning environment, it is important to be able to correctly assess students to help them receive the best possible education. This can have a big impact on the way disabled students learn and their goals for gaining a high level of qualification. This paper investigated different fuzzy logic-based techniques for student academic evaluations in a 3D virtual learning environment (VLE). Some of the techniques were found to be especially helpful for disabled students, and the paper also described the development and design of evaluation systems that take this into account. The study used fuzzy logic to study how well disabled students are doing in their classes over a whole year. This fuzzy logic was developed using MATLAB software, which uses features extracted from student evaluations. Disabled students’ characteristics (such as experience and understanding, problem-solving skills, etc.) have been measured and combined with a 3D virtual learning environment built using open-source software, Moodle, and Sloodle. This way, disabled students can interact with their courses inside a 3D VLE using Sloodle. According to the findings, which were based on 20 disabled students, fuzzy logic technology used in 3D Virtual Learning Environments (VLEs) produces different results than traditional assessments. The difference between the two is about 3.9 points on average.

Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network

“…A Mamdani-type fuzzy inference system (FIS) was chosen for the FLM due to its ability to incorporate expert knowledge [25,31]. This is particularly useful when dealing with the value range classifications of linguistic variables, such as low temperature, optimal temperature, etc.…”

“…This design ensures a comprehensive assessment of SQ and MC, accounting for the gradual changes in input parameter values. The well-defined MFs play a crucial role in capturing the inherent uncertainty and providing accurate evaluations within the FIS [25,31].…”

“…These AI models analyze the collected data and generate results that provide a more comprehensive understanding of the variable status and behavior. By using AI-interpreted data, monitoring systems can offer a more accessible and user-friendly approach to gaining meaningful insights and making informed decisions [25][26][27][28].…”