This complete article of the research category presents proposes the application of Educational Data Mining (EDM) techniques using data generated by a web-based Intel-ligent Tutoring System (ITS) developed for teaching algebra. The aim is to classify students' academic performance, seeking evidence to assist teachers in the decision-making process. To achieve this goal, we utilized the Knowledge Discovery in Databases (KDD) methodology to analyze students' academic data and identify patterns, trends, and relationships that can be used to enhance the teaching and learning process and make informed decisions in the educational context. Practical results revealed that the Random Forest algorithm classified students' academic performance in each evaluation period with an accuracy of approximately 94 %. These findings derived from data analysis can serve as a basis for decision-making, aiding the teaching process for teachers, tutors, and administrators, as well as monitoring academic performance and identifying challenges present in each class. In summary, these results provide a comprehensive guide to the use of EDM techniques in a specific context.

This research paper discusses the impact of teaching and learning technologies on generating extensive data in education. Despite this data, a significant portion remains unanalyzed, creating a gap in utilizing its potential insights. Educational dashboards address this issue by presenting student information and interactions in online learning environments, utilizing Educational Data Mining (EDM) techniques. EDM aims to understand student behavior, enhance teaching processes, identify learning patterns, and personalize content. It aids in performance analysis, helping struggling students and guiding teaching adjustments. The study proposes an educational dashboard for analyzing Intelligent Tutoring System (ITS) data, specifically in algebra teaching. Using decision tree algorithm to extract insights from student interactions. The developed web dashboard organizes and visualizes student performance data, aiding monitoring, decision-making, communication, and transparency. Educational dashboards enhance decision-making, data analysis, and communication, benefiting both students and the educational system's performance.

This full paper of the research category describes the ERPLab as an innovative educational platform that aims to promote inclusivity and diversity in programming and robotics. It provides a unique learning experience, particularly in remote or resource-limited areas, making scientific education more accessible to a broader audience. ERPLab offers students an understanding and integration of scientific knowledge by combining biology, engineering, and programming. ERPLab plays a significant role in educational technology research by exploring new possibilities for remote teaching and providing valuable data and insights to evaluate the effectiveness of this approach. One of the standout features of ERPLab is its use of an automated greenhouse controlled by a 3D printer-produced robotic arm equipped with sensors, and two Wi-Fi cameras, esp32cam, facilitating observation from multiple angles, allowing students to explore new avenues of scientific learning. This system enables students to learn about plant cultivation while developing their programming and technological skills. ERPLab combines advanced technology with natural repellent plants to promote innovative teaching and research methods. The plant selection within the ERPLab includes natural repellents like rosemary (Rosmarinus officinalis), rue (Ruta graveolens), and basil (Ocimum basilicum). These essential programming skills are directly applicable to the rapidly advancing INDUSTRY 4.0. Some preliminary work was carried out in a school environment, and the results of feedback from some students and teachers were promising regarding the proposed approach. For future work, we will carry out qualitative and quantitative research to observe the interest and development of users for the program of the platform in the context of regular classes.

This complete article of the research category presentes proposes the application of Educational Data Mining (EDM) techniques, based on data generated by a web-based Intelligent Tutor System (ITS), developed for the teaching of algebra, which helps students to develop fundamental knowledge of mathematics, in addition to carrying out a comparative study between the level of difficulty of the proposed algebraic questions. For this, we use the knowledge discovery methodology to perform the following steps from the data: cleaning, integration, selection, transformation, mining, evaluation and presentation of information. The practical results reveal that the proposed architecture can classify the academic performance of students in each evaluation period with an accuracy of around 80%. It was also possible to identify factors related to the resolution of questions, such as the rate of correct answers and the mathematical steps to solve an exercise, classifying the level of difficulty of the questions proposed by the system, acting on the student’s deficiencies, and the system. The results obtained from the data analysis can serve as a basis for decision-making, helping in the teaching process for teachers, tutors and managers to monitor academic performance, enabling the correction of problems and identifying the individual and collective difficulties present in each class. In summary, these results also provide a general roadmap on the performance of using EDM techniques in a given context.