Chapter 5 Prediction Rules in E-Learning Systems Using Genetic Programming Amelec Viloria, Mercedes Gaitan Angulo, Sadhana J. Kamatkar, Juan de la Hoz – Hernandez, Jesús García Guiliany, Osman Redondo Bilbao and Hugo Hernandez-P Abstract This paper describes the use of Data Mining Techniques to improve teach- ing–learning processes in the linear programming course offered at the Engineering Faculty at Mumbai University, India. The proposed approach seeks to model the student’s interaction with the study material using prediction rules whose interpre- tation will allow to detect the weaknesses of the educational process and evaluate the quality of the study material. The proposed rule discovery method is the Evo- lutionary Algorithms and particularly the Grammar-Based Genetic Programming (GB-GP), which is compared to association rules and decision tree construction for discovering prediction rules. Keywords Data mining techniques · E-learning · Evolutionary algorithms · Grammar-based genetic programming (GB-GP) A. Viloria (B ) Universidad de la Costa, St. 58 #66, Barranquilla, Atlántico, Colombia e-mail: aviloria7@cuc.edu.co M. G. Angulo Corporación Universitaria Empresarial de Salamanca (CUES), Barranquilla, Colombia e-mail: m_gaitan689@cues.edu.co S. J. Kamatkar University of Mumbai, Mumbai, India e-mail: sjkamatkar@mu.ac.in J. de la Hoz – Hernandez · O. R. Bilbao · H. Hernandez-P Corporación Universitaria Latinoamericana, Barranquilla, Colombia e-mail: juandelahoz03@gmail.com O. R. Bilbao e-mail: oredondo@ul.edu.co H. Hernandez-P e-mail: hugohernandezpalma@gmail.com J. G. Guiliany Universidad Simón Bolívar, Barranquilla, Colombia e-mail: jesus.garcia@unisimonbolivar.edu.co © Springer Nature Singapore Pte Ltd. 2020 V. Vijayakumar et al. (eds.), Proceedings of 6th International Conference on Big Data and Cloud Computing Challenges, Smart Innovation, Systems and Technologies 164, https://doi.org/10.1007/978-981-32-9889-7_5 55