Moocs Video Mining Using Decision Tree J48 and Naive Bayesian Classification Models

By OTHMAN EL HARRAK, Abderrahim GHADI, Jaber EL BOUHDIDI

Abstract


Nowadays, the internet has become the first source of information for most people, it plays a vital role in the teaching, research and learning process. MOOCs are probably the most important "novelty" in the field of e-learning of the last years, it represents an emerging methodology of online teaching and an important development in open education. MOOCs makes it possible for everyone to access to the education over the world, but due the large resources in the web, it becomes increasingly difficult for a learner to identify a suitable course for him. This task can be tedious because it involves access to each platform, search available courses, select some courses, read carefully each course syllabus, and choose the appropriate content.  Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches for web video mining using traditional image processing (signal processing) and metadata based approach.  In this work, effective attempts will classify and predict the metadata features of web videos such as category of the Mooc video, length, number of comments, rate, ratings information and view counts of the Mooc videos. In this perspective, the data mining algorithms such as Decision tree J48 and naive Bayesian algorithms will be used as a part of Mooc video mining. The results of Decision tree J48 and naive Bayesian classification models will be analyzed and compared as a step in the process of knowledge discovery from Moocs videos.

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