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

Outlier detection and characterization for students

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceNowadays, web-based educational systems are being used as an essential tool to the learning process and with them the universities are capable to collect data from the students and build studies to understand their behavior. In these studies, the outliers’ students are not the main topic, or they are removed because of their extreme behavior, so this thesis focuses on detecting the outliers and understanding their behavior. Focusing on these students a methodology is proposed to find two clusters using outliers’ techniques on their grades and Moodle usage, the main goal was to detect the subjects with a high volume of usage in Moodle and with a bad performance on the final grade, this analysis was done for each subject that the student did. In the end we detect a total of five students that require an intervention by the university to understand how their performance could improve or if they are having some troubles in the use of the online tool. The methodology proposed shows an efficient and easy way to find the students and could be replicated easily for other universities and be implemented as an active tool to assist the university

Contribuciones a la predicción de la deserción universitaria a través de minería de datos

Identifica una limitada producción científica que analiza factores de deserción desde la perspectiva del estudiante, que es el actor principal de la deserción, y la construcción de modelos híbridos de predicción que permitan comprender mejor manera el problema de la deserción en las universidades. El objetivo consiste en contribuir al proceso de predicción de la deserción estudiantil universitaria a través del estudio integral de factores, técnicas y herramientas de minería de datos usados con este fin. Se concluye que la predicción de la deserción en las universidades puede variar, ya que dependerá de los factores de ingreso, del contexto educativo estudiado, del entorno de educación aplicado, y de los antecedentes de los estudios para los que fueron usados. Por otro lado, se considera importante determinar si es suficiente con predecir la deserción o si se requiere incorporar estudios que establezcan estrategias para mitigar la deserción en las instituciones de educación superior.Tesi