• Anil Kumar Tiwari,
• Sanjay Kumar

Massive Open Online Courses (MOOC) is an extensive way of providing online education to the students all over the world. According to statistics, this educational system has millions of students enrolled in hundreds of courses across a variety of programmes. Since its inception, MOOC has faced a number of issues, one of which is known as the "student dropout ratio," which is also a significant difference between traditional teaching and MOOC. As a result of this fact, MOOC's overall performance has a negative impact on the true purpose of distance learning. In MOOCs, however, the gap between course registration and course completion is quite large. On the plus side, emerging technologies have opened up several opportunities for students to receive education online; however, due to a variety of factors, the dropout rate of online students is higher than that of traditional school students. The goal of this study is to better understand and predict the MOOC dropout rate. The multiple models and evaluation metrics generating variety of results as extracted from literature review. In the second model, a hybrid cluster based feature selection model is implemented in order to optimize the class prediction. In this model, a hybrid cluster based metaheuristic model is designed and implemented on the classification problem.

MOOC dropout prediction; MOOC data; KDD dataset; machine learning algorithm.

"An efficient cluster based feature section and classification framework for student dropout prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 12, page no.b68-b80, December-2022, Available :http://www.jetir.org/papers/JETIR2212109.pdf

"An efficient cluster based feature section and classification framework for student dropout prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 12, page no. ppb68-b80, December-2022, Available at : http://www.jetir.org/papers/JETIR2212109.pdf