Estimating Expected Student Academic Performance

• Walter Orozco ^{[1] [2]} ; Miguel Ángel Rodríguez-García ^[1] ; Alberto Fernández ^[2]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
  2. [2] Universidad Estatal Península de Santa Elena

     Universidad Estatal Península de Santa Elena

     Ecuador

     
• Localización: The 11th International Conference on EUropean Transnational Educational: (ICEUTE 2020) / Álvaro Herrero Cosío (ed. lit.) , Carlos Cambra Baseca (ed. lit.) , Daniel Urda Muñoz (ed. lit.) , Javier Sedano Franco (ed. lit.) , Héctor Quintián Pardo (ed. lit.) , Emilio Santiago Corchado Rodríguez (ed. lit.) , 2021, ISBN 3-030-57798-8, págs. 121-131
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • In recent decades, society has a primary need for improving edu-cation systems. Predicting the performance of students has become a referencetopic very analyzed by the research community. Currently, there are severalcutting-edge technologies that make very easy to collect educational data ininstitutional systems due to the new information management systems where almost everything is digitalized. The analysis of this information offers uniqueopportunities that have a direct impact on students, instructors and academicinstitutions programs. Concretely, we propose a modular system to evaluate teaching performance by considering several primary factors related to studentslearning process. In this work, we present thefirst module, a statistical modelthat aims at obtaining the expected student’s achievements in a particular course.Wefirst analyzed students’performance primary factors on Higher Education Systems. To identify these factors, we have conducted a literature review. Then,we examine three different techniques to build the prediction model: Multiple Linear Regression (MLR), Support Vector Machine (SVM) and ArtificialNeural Networks (ANN). To select the suitable technique, we carried out avariety of experiments by using a real dataset from University of Santa Elena(Ecuador). The achieved experiments revealed promising results.