Student profiling model for the "Computer Programming" course

Abstract. The research work presents a student profiling model, and the results after applying it to the "Computer Programming" course, which is developed partially virtual through the Virtual Intelligent Learning Platform, this is an E-Learning system that allows the students to consult the course material and to take the tests. The model identifies the profiles based on socio-economic data (age and gender), and students' behavior when using the Platform (Number of accesses to documents, exercises or videos, percentage of accesses performed in class, average session length and average absence time). The profiles found are analyzed in order to define if they are connected to the academic performance. Data of around 1000 students (those enrolled in 2014) and 20500 sessions, were used. The profiles were found through the k-Means clustering algorithm. Per each profile, the common sequences of navigation were identified. A warnings system is proposed, it uses a lazy classifier to assign a profile to the current student, and based on this profile, give timely feedback by showing alerts. A recommender system is proposed, it shows suggestions of resources that should be accessed, in order to improve the academic performance.

• Autores: Peñuela Vega, Camilo Orlando
• Fecha de publicación: 2015
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/56000
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/56000; http://bdigital.unal.edu.co/51554/
• Palabra clave: 0 Generalidades / Computer science, information and general works; 37 Educación / Education; 62 Ingeniería y operaciones afines / Engineering; Minería de datos; Minería educativa; Entorno virtual de aprendizaje; Data mining; Educational data mining; Learning management system
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional