Analysis of Academic Performance in massive Open Online Courses (Moocs) Using Process Mining
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Mahesh T R, Dr.B Mohan Kumar Naik|
|DOI : 10.14445/22312803/IJCTT-V68I12P105|
How to Cite?
Mahesh T R, Dr.B Mohan Kumar Naik, "Analysis of Academic Performance in massive Open Online Courses (Moocs) Using Process Mining," International Journal of Computer Trends and Technology, vol. 68, no. 12, pp. 21-25, 2020. Crossref, 10.14445/22312803/IJCTT-V68I12P105
The purpose of this paper is to provide a survey of academic performance in massive open online courses (MOOCs) to improve students` learning experience. Due to the large volume of data in educational databases of Student`s data, e.g., weekly evaluation grades or points, demographic variables such as age, ethnicity, and sex and weekly interaction data based on event logs, e.g., video lecture interaction, submission time of assignment solution, amount of time spent weekly driven this design, Automated Student performance prediction is a very important task. This study compares the four distinct logistic regression techniques for machine learning classification, Naïve Bayes (NB), (LR), random forest (RF), and K-nearest neighbor, to track the performance of students every week and to predict their overall performance. While MOOCs provide a versatile learning platform, they are prone to early dropouts and low completion rates. This research focuses on a data-driven plan to enhance students` learning experience and dramatically reduce the dropout rate. Early forecasts based on individuals` involvement will help educators provide students who are currently struggling in the course with proper support.
Prediction, MOOCs, Machine learning, Learning analytics, Process mining, Education data mining
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