| Abstrakt: |
In recent years, the MOOC (Massively Open Online Courses) revolution has transformed the distance learning landscape. Based on the distribution of educational content, this type of education is expected to undergo the same revolution as all the traditional sectors of content and service sales, such as music, video and commerce, due to the emergence of new technologies. However, the completion rate remains a key measure of a MOOC's success, as the number of students enrolling in a MOOC typically drops during the course. This rate can reach 2-10% at the end of the course. Therefore, dropout prediction is an excellent way to identify at-risk students and make timely decisions. In this study, a prediction model is developed using one of the most widely used methods, the artificial neural network (ANN). As a result, our model can be considered as an optimal option in terms of accuracy and suitability for predicting dropouts in MOOC. [ABSTRACT FROM AUTHOR] |
