Analysis of student performance through data mining techniques. Study case: Learning management system at UET

By, Jugerta GURABARDHI, Teuta XHINDI

Abstract
Industry 4.0, like in any other field, also in education, has made it possible for the activities of teaching to continue to develop and the efforts made are for distance learning or e-learning. This is related to the integration of systems that synchronize with computers, mobile phones and technology that can manage the learning system electronically.
Nowadays, modeling user preferences is one of the most important tasks challenging in e-learning systems. This research aims to use Data Mining (DM) for it analyzed the data collected from the learning management system (LMS) used in e-learning systems. The main goal is to predict the individual learning style by using the Moodle LMS platform and analyze the data through Data Mining techniques. With a large volume of data, such as the time spent on the page, as well as the actions taken by students on the platform, it is intended to adapt models to their current preferences. In this context, the research focuses on the use of Data Mining to improve the quality of education and identify models in educational environments. For her to accomplish this, the study uses well-known data mining techniques and uses an environment analysis called RapidMiner. The study describes how RapidMiner can be used to extract information from the raw data of students in the management system to the students. This paper uses student data captured in the UET LMS management system online teaching and analyzes different algorithms to choose the most suitable ones for the given model. In particular, the analysis of 10 million data records was carried out usage collected from the Learning Management System in 450 online university courses from the period March-June.
Keywords: Student Performance, Data Mining, Learning Management System, Analysis, Forecasting, RapidMiner, LMS.

https://doi.org/10.58944/baja2105