YouTube aware Personalized Ranking System for Future ICT Education

With the various social services such as Facebook, Twitter and YouTube, many kind of bit data types are exponentially increasing. Therefore, people have difficulties to find the sub-optimal service among the overflow data in Internet. In education area, new trend with various ICT tools such as YouTube, STEAM (Science, Technology, Engineering, the Arts and Mathematics) supportive computational thinking software is famous. In this paper, we propose YouTube aware personalized recommender system for future ICT education. It is a technique to automatically rank high - visibility channels and videos in YouTube based on the personalized learning topic. To do this, we analyze the relevance of statistics such as views, likes, subscribers, and comments in YouTube, and use the R programming language with the rvest, ggplot2, stringr packages and the Chrome extension called SelectorGadget for direct data crawling. Initial experiments confirmed the possibility of the proposed technique and confirmed the possibility of extension.