Investigation of the relationship between technology use and student success with some clustering algorithms

Machine learning, which is a sub-step of artificial intelligence, has gained great importance in the field of education in recent years. Studies such as student-teacher interaction, measuring academic achievement and evaluating the attention of the student in the lesson can be done by using machine learning applications. At the same time, it is very effective in evaluating the success of students in the processing and analysis of data using data mining. In this thesis study, the "Information Technologies Utilization Scale" was applied to the 5th and 6th grade students studying at Hürriyet Yıldız Schools and Boğaziçi Schools in the Atakum district of Samsun. Using the decision tree, random forest, x-means, k-means and k-medoid clustering algorithms in the RapidMiner program, the relationship between the answers given by the students in the scale and their academic achievement was estimated.