Machine learning prediction and analysis of students’ academic performance

The aims of this research were to develop a machine learning prediction Decision Tree classification model and analyze the success of engineering students based on their performances during secondary school education. The success of students was analyzed and measured as a binomial response to whether students successfully finished the first and the second study years. The developed model examined general success, number of awards obtained at competitions, special awards, average grades in mathematics, physics, and one of the official state languages during secondary school as predictor variables. General success was defined by summing up students’ grade point averages (GPA) of each school year. The number of courses transferred from the first into the second study year and students’ GPA obtained during the first study year were added as predictor variables in the analysis and development of a prediction model for the student’s success during the second study year and their enrollment in the third study year. Data showed that majority of the students enrolled in the first study year were gymnasium or technical high school graduates. Developed machine learning prediction model showed that for the success of enrolled students in the first study year General Success of students during secondary school is the most important predictor variable, followed by mathematics and physics grades. However, for the success of the students enrolled in the second study year the most important predictor variable was number of the courses transferred from the first into the second study year, followed by students’ GPA obtained during the first study year and General Success. Machine learning Decision Tree classification modeling was shown to be an adequate tool for the prediction of the success of engineering students during the first and second study years

Implementación de minería de datos para el análisis del desempeño estudiantil basado en los recursos y actividades del entorno virtual de aprendizaje del SIIU-UTN

Implementar minería de datos para el análisis del desempeño estudiantil basado en los recursos y actividades del Entorno virtual de aprendizaje del SIIU-UTN.El desempeño académico de los estudiantes forma parte de los aspectos relevantes a tratar en relación con la calidad de la Educación de una institución, además constituye un indicador de la realidad educativa del establecimiento. El presente trabajo permitió identificar factores que influyen en el rendimiento académico, patrones de uso-rendimiento, reglas de asociación y generar un modelo de predicción del éxito académico, por medio de la aplicación de técnicas descriptivas y predictivas de minería de datos. Se analizó un conjunto de datos conformado por datos socioeconómicos, académicos y interacciones con el Entorno Virtual de Aprendizaje (EVA) de los años 2017 a 2018, con total de 26 atributos y 57 115 instancias. Aplicando la metodología de descubrimiento de conocimientos en bases de datos (KDD) se desarrolló cada una de las fases de la minería de datos. Los resultados más relevantes mostraron que las notas de las parciales o evaluaciones, porcentaje de faltas, la entrega de pruebas y trabajos son factores que influyen en el desempeño académico.Ingenierí

Incremental and Adaptive Fuzzy Clustering for Virtual Learning Environments Data Analysis

Virtual Learning Environments (VLE) offer a wide range of courses and learning supports for students. Such innovative learning platforms generate daily a huge quantity of data, regarding the interactions among the students and the VLE. To analyze these big educational data a new research branch called educational data mining (EDM) has emerged, that puts together computer scientists and pedagogues researchers' expertise. So far, educational data have been studied as stationary data by traditional machine learning methods. Rather, educational data are non-stationary in nature and can be better analyzed as data streams. In this paper we investigate the use of an adaptive fuzzy clustering algorithm called DISSFCM (Dynamic Incremental Semi-Supervised FCM) to process educational data as data streams and predict the students' outcomes to one exam module. Numerical experiments on the Open University Learning Analytics Dataset (OULAD) show the reliability of DISSFCM in creating good classification models of educational data