Eryılmaz, MeltemAl-Khafaji, MustafaEryilmaz, MeltemComputer Engineering2024-07-052024-07-0520220978303089880997830308987932367-33702367-338910.1007/978-3-030-89880-9_312-s2.0-85119860706https://doi.org/10.1007/978-3-030-89880-9_31https://hdl.handle.net/20.500.14411/1630This study applies two artificial intelligence methods represented by both the neural network and fuzzy logic to predict student achievement in the exam. The dataset used in this study was taken from an Iraqi engineering college and it represents data of 200 students who have enrolled in the computer science course. Gender, age, resources downloaded, videos viewed, discussion chat joined, exam scores used as the data set. The type of artificial neural network used was pattern neural network. Levenberg-Marquardt's algorithm was used to train the neural networks. On the other hand Sugeno fuzzy inference system was used for the fuzzy logic. The study results showed that the students who spend more time on the learning system have the most success rate. According to the results the neural network accuracy rate 73% and the fuzzy was 88%. This high accuracy rates support that artificial intelligence methods can be used to predict student academic achievement.eninfo:eu-repo/semantics/closedAccessArtificial intelligenceArtificial neural networkFuzzy logice-LearningConference ObjectQ4359403414WOS:000793730500031