Learning Analytics Model for Predictive Analysis of Learners Behavior for an Indigenous MOOC Platform (Tadakhul System) in Oman

Abstract

The Massive Open Online Courses (MOOCs) platforms are widely used by the learner community all over the world. Using MOOC, the learners can choose the course of interest and learn it in their own pace. The main problem encountered in most MOOC based learning is the lack of a learning analytic system that monitors the learners’ interaction with their enrolled courses. This problem leads to incompletion or discontinuation of the course. In this paper, ‘Tadakul’ system, an original bilingual (English and Arabic) MOOC platform is developed for students of higher education institutions of the Sultanate of Oman. Using the Tadakhul system, learners of various higher education institutions of Oman can enroll themselves in various courses of their interest and complete the course at their own pace. This research aims in understanding the impact of learning analytics system used in the Tadakhul system. A novel deep-learning approach used in the system monitors the learning process of the learners based on their interaction with the enrolled courses. The feedback thus obtained, using the deep-learning approach, can be used to improve the student learning experience. To analyze the feedback obtained from the learners, an innovative approach of combining Bidirectional Long Short-Term Memory (BiLSTM) network with a Convolutional Neural Network (CNN) is proposed. The BiLSTM model performs well in analyzing the sequential data whereas the CNN model is good in extracting the spatial features at each hidden layer that are important for evaluating the student’s learning patterns. Thus the proposed model can identify learner’s learning behavior and learning styles that help teachers better understand the individual needs. The results of the experimental study revealed that the proposed model outperformed conventional machine learning approaches in predicting learner’s learning behavior. Hence, the results obtained by integrating BiLSTM and CNN models on the Tadakhul platform can improve the student experience by making teaching more efficient and effective.