Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
26 January 2020 |
10 February 2020 |
10 March 2020 |
Abstract: MOOC opens up the doors for universal access to education remotely and serves as a constructive approach to acquire formal education in an informal way by negating the traditional practices. In recent years, the number of MOOC video resources has increased exponentially. Therefore, the need of the moment is a fully automated system that would proficient enough to store, analyze and manage such immensity of videos while sustaining the quality in response. An automatic classification/prediction of videos is a challenging and complex aspect, although, supervised machine learning can achieve this task in an effective way. Many applications use text classification to categorize documents like e.g. spam filtering, email routing, sentiment analysis, etc. In this study, we present a competent and adaptive technique for autonomous classification of MOOC videos transcription using natural language processing and machine learning model. Our approach is capable to predict the category of a targeted video; the data mining algorithms such as SVM, Random Forest, and Naive Bayesian will be engaged to organize the MOOC videos. Experiments reveal that our approach outperformed other approaches in the field of transcription classification and supervised learning.
