Machine Learning based Fast Reading Algorithm for Future ICT based Education
Hyonam Jeon, Hayoung Oh, Jaejun Lee · 2018
With the development of ICT and Big Data, a new education paradigm has been attracting attention by utilizing techniques to efficiently grasp large amount of articles and fairy tales and so on. For example, even in the same event and subject, new version-type articles or fairy tales are pouring out of myriad times and regions. This paper proposes machine learning based fast reading algorithm to identify elements of an important story that are handed down in spite of temporal and spatial differences using the version of 72 similar folk tales of the folk tales "Red Hat" which exist in Europe, Asia, Africa etc. To do this, we analyze the factors depending on the existence of various versions in a decision tree and conduct research using R language tree and caret package. Through the evaluation of the analytical model, we confirmed the existence of the unchanging core elements of traditional talks, which are handed down to the constraints of time and space, and the possibility of a model that intuitively understands them. The result of this study is expected to be used as a new educational field for ICT - based computing thinking (CT).