A Neural Network-Based System to Predict Early MOOC Dropout

In recent years, MOOC (Massively Open Online Courses) revolu-tion has transformed the landscape of distance learning. Based on the distribution of educational content, this type of education is expected to undergo the same revolution as all of the traditional sectors of content and service sales, such as music, video, and commerce, due to the emergence of new technologies. How-ever, the completion rate remains a key metric of MOOC success as the number of students registering for a MOOC usually decreases during the course. This rate can reach 2 to 10% at the end of the course. Therefore, predicting dropouts is an excellent way to identify students at risk and make timely decisions. In this study, a prediction model is developed using one of the most widely used methods, the recurrent neural network (RNN). As a result, our model can be considered as an optimal option in terms of accuracy and fit for predicting dropouts in MOOCs

Towards an Adaptive Learning Model using Optimal Learning Paths to Prevent MOOC Dropout

Currently, massive open online courses (MOOCs) are experiencing major developments and are becoming increasingly popular in distance learning programs. The goal is to break down inequalities and disseminate knowledge to everyone by creating a space for exchange and interaction. Despite the improvements to this educational model, MOOCs still have low retention rates, which can be attributed to a variety of factors, including learners’ heterogeneity. The paper aims to address the issue of low retention rates in MOOCs by introducing an innovative prediction model that provides the best (optimal) learning path for at-risk learners. For this purpose, learners at risk of dropping out are identified, and their courses are adapted to meet their needs and skills. A case study is presented to validate the effectiveness of our approach using classification algorithms for prediction and the ant colony optimization (ACO) algorithm to optimize learners’ paths