A Review on the Learner's Performance Prediction Techniques in MOOC Courses through Data Mining

ABSTRACT

Nowadays, Massive Open Online Courses are in demand owing to their informative value, easy access and low costs. The Covid-19 pandemic era saw a lot of teaching and learning through the online resources. One of the developing fields is Educational Data Mining in which the data derived from the educational environments is collected in databases, which is further analyzed to extract some interesting patterns of information. The findings can aid in supporting the educational staff in designing a cohort that may produce better results in terms of increasing the learner's performance, identifying at-risk students, placement prediction and dropout prediction, whatever the current motive may be. In this paper, we emphasize on the techniques focusing on the performance prediction that have been applied during the years 2012 to 2022 and the attributes affecting the performance have been determined. © 2022 IEEE.