Kaya, Irem Ersoz2025-03-172025-03-1720190218-21301793-6349https://doi.org/10.1142/S0218213019400049https://hdl.handle.net/20.500.13099/1761In the context of learning analytics, machine learning techniques have been commonly used in order to shed a light on solving educational problems. The studies can be associated with the curriculum design, mostly at course level, which target to improve the learning and teaching processes. In this study, the relationship between the courses was analyzed via artificial neural network (ANN) to provide a support for curriculum development process at a program level. Extracting the dependence among courses within a program plays a key role in placing them coherently to ensure the success of curriculum. For this purpose, it was investigated if the performance of students in a subsequent course is influenced from the performance in some previous basic courses. The results demonstrated that the performance relations could be used to describe information for prioritization and sequencing of courses within a program. In addition, ANN can successfully predict the student performances leading to find out the relationships between these courses. The application of the multilayer feedforward neural network resulted in an achievement of a prospering prediction performance based on the grades of prerequisite courses with 87% accuracy rate without sacrificing a significant sensitivity.eninfo:eu-repo/semantics/closedAccessLearning analyticsartificial neural networksmultiple regression analysisstudent academic performancesprior and prerequisite coursescurriculum developmentArticle10.1142/S0218213019400049284Q4WOS:0004786344000052-s2.0-85070316678Q3