@article{2019,title={INVESTIGATION OF THE FACTORS AFFECTING THE CHOICE OF MOOC VIDEOS BY DATA MINING TECHNIQUES},abstractNode={The study aimed to explore the factors that should be considered in the preparation of MOOC videos by using data mining techniques is a quantitative research. The study consists of three steps: i) development of a scale to determine the factors, ii) data collection and iii) data analysis. Within the scope of the study, Khan Academy MOOC videos from YouTube were examined. In addition to a general decision tree, the decision trees for the dependent variables such as likes, dislikes, comments and views were formed. This decision tree can be used to prepare MOOC videos which can be viewed, commented and liked more. Gini and C5.0 decision tree algorithms were applied to the training videos data set using the R programming language. In the general decision tree, it is seen that “written elements” attribute (coded as YUN) is a root node. If the YUN value is less than 0.8, the videos are considered bad. On the other hand, “the number of likes” played a big role, if the YUN values greater. According to the findings, although the C5.0 algorithm is more successful, it has also achieved more than 70% performance with Gini algorithm.},author={Güler KARAMAN-- Asiye ATA},year={2019},journal={The Journal of Social Sciences}}