Secure Learning Management System Based on User Behavior

Predicting Students at Risk of Dropout in Technical Course Using LMS Logs

Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that correlates with success or failure in completing the course. Six algorithms were employed, with models trained at three different stages of their two-year course completion. We tested the models with records of 394 students from 3 courses. Random Forest provided the best results with 84.47% on the F1 score in our experiments, followed by Decision Tree obtaining similar results in the first subjects. We also employ clustering techniques and find different behavior groups with a strong correlation to performance. This work contributes to predicting students at risk of dropping out, offers insight into understanding student behavior, and provides a support mechanism for academic managers to take corrective and preventive actions on this problem.

Investigating the Attitudes of First-Year Students of the Faculty of Physical Education and Sports of Galati towards Online Teaching Activities during the COVID-19 Pandemic

The paper identifies the perceptions of first-year undergraduate students from the Faculty of Physical Education and Sports in Galati on online teaching activities, dominant and needful in the current pandemic context. The questionnaire used contains 23 items and was structured based on four distinctive factors, namely attractiveness, accessibility, motivation and efficiency; it was applied after the winter session of the academic year 2020–2021. The values of the internal consistency coefficient Cronbach’s alpha indicate for the four mentioned factors a high fidelity for the measurements of the investigated features. The results of the 147 completed questionnaires allowed the definition of the independent variables sex (boys and girls) and environment of origin (rural and urban) the identification of their influence on the scores of each item (dependent variables) by using the statistical technique MANOVA (multivariate and univariate analysis), besides the values of F and the corresponding significance thresholds; the magnitude of the effect, expressed by partial eta squared (η2p), was also calculated. Even if the averages of item scores differ between sexes and backgrounds, the differences noted are in few cases significant: attractiveness and socialization for those in urban areas; participation in activities and effective involvement for girls; technical deficiencies, platform logging and weak computer skills for those in rural areas; and an increase in free time for girls and students in urban areas. The study undertaken allows the identification of the favorable aspects and the shortcomings of online teaching activities, these being the premises for optimizing the teaching process in the following stages.

A Hands-On Laboratory for Intelligent Control Courses

This research focused on developing a methodology that facilitates the learning of control engineering students, specifically developing skills to design a complete control loop using fuzzy logic. The plant for this control loop is a direct current motor, one of the most common actuators used by educational and professional engineers. The research was carried out on a platform developed by a group of students. Although the learning techniques for the design and implementation of controllers are extensive, there has been a delay in teaching techniques that are relatively new compared to conventional control techniques. Then, the hands-on laboratory offers a tool for students to acquire the necessary skills in driver tuning. In addition to the study of complete systems, the ability to work in a team is developed, a fundamental skill in the professional industrial area. A qualitative and quantitative analysis of student learning was carried out, integrating a multidisciplinary project based on modern tools.