Many institutions in the field of education have been involved in distance education with the learning management system. In this context, there has been a rapid increase in data in the e-learning process as a result of the development of technology and the widespread use of the internet. This increase is in the size of large data. Today, big data can be primarily processed, the relationships between data can be discovered, a meaningful conclusion can be drawn, and predictions about the future using big data can be made. However, these data are generally not used in a way to contribute to the people and institutions (educators, education administrators, ministries, etc.) involved in the education process. Therefore, this study aims to estimate the academic success of students who receive education in the distance education process using data mining methods. The reason why data mining is used is that these methods are particularly effective and powerful tools in classification and prediction processes. The methods used in the study are Random Forest, Artificial Neural Networks, Naive Bayes, Support Vector Machines, Logistic Regression, and Deep Learning algorithms, respectively. The dataset includes primary, secondary, and high school students' data, which were obtained from the learning management system used in the distance education process. As a result, the study findings showed that Deep Learning, Random Forest, and Support Vector Machines algorithms provide prediction success at higher performance than others.
Depression is a disease that causes physiological and psychological problems. Depression causes in individuals, sleep disturbance, constant fatigue, anorexia, inability to do daily activities, and feeling constantly tired and tired. Among the causes of depression; sociological, biological and psychological conditions are counted. The first step in treating depression is to make the correct diagnosis. Beck Depression Inventory (BDI) is a self-report scale consisting of 21 questions that evaluates the severity of depressive symptoms and the risk of depression. The purpose of BDI is not to define a diagnosis of depression, but to objectively quantify the degree of depression. The aim of this study is to determine the most successful algorithm from artificial neural network algorithms by using a data set of BDI scale. Random Forest, Decision Tree, Naive Bayes and Neural Network methods were used in the prediction model of diagnosis and severity of depression. The most appropriate estimation algorithm for problem solving has been determined. The best result; the training rate was 99.9%, the test rate was 98.5%, and the loss rate was 0.1% for training and 1.5% for testing, using the "Artificial Neural Network" algorithm. The lowest rates were obtained with the "Decision Tree" algorithm, with 90.8% training and 87.1% test rates. In addition, different results were obtained with Adam, SGD and L-BFGS-B optimizations used in ANN algorithms and the best success percentage was obtained as a test result in Adam technique.
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