Bulut, FarukDonmez, IlknurInce, Ibrahim FurkanPetrov, Pavel2025-03-262025-03-2620241300-915X10.55020/iojpe.1390421https://doi.org/10.55020/iojpe.1390421https://search.trdizin.gov.tr/tr/yayin/detay/1230747https://hdl.handle.net/20.500.14704/511A homogeneous distribution of students in a class is accepted as a key factor for overall success in primary education. A class of students with similar attributes normally increases academic success. It is also a fact that general academic success might be lower in some classes where students have different intelligence and academic levels. In this study, a class distribution model is proposed by using some data science algorithms over a small number of students’ dataset. With unsupervised and semi supervised learning methods in machine learning and data mining, a group of students is equally distributed to classes, taking into account some criteria. This model divides a group of students into clusters by the considering students’ different qualitative and quantitative characteristics. A draft study is carried out by predicting the effectiveness and efficiency of the presented approaches. In addition, some process elements such as quantitative and qualitative characteristics of a student, data acquisition style, digitalization of attributes, and creating a future prediction are also included in this study. Satisfactory and promising experimental results are received using a set of algorithms over collected datasets for classroom scenarios. As expected, a clear and concrete evaluation between balanced and unbalanced class distributions cannot be performed since these two scenarios for the class distributions cannot be applicable at the same time.eninfo:eu-repo/semantics/openAccessEğitimEğitim AraştırmalarıEğitimÖzelArticle52133123074713