Decision tree based prediction system for word difficulty classification

Abstract

In the era of information technology in education, personalised learning is becoming increasingly important, especially in the English learning process. An important aspect of facilitating personalised learning in English reading is the use of a reliable objective word difficulty classification system to quickly capture and understand reading difficulties and core concepts. The aim of this study was to use decision tree modelling to predict the general proficiency of the public in words with different attributes. In addition, a K-Means clustering algorithm was used to categorise words into five classes based on their level of difficulty. By adopting this approach, the prediction of word difficulty becomes both fast and objective, and by testing the accuracy of the model, it was found that our model achieved an accuracy of 0.95. Accurate classification of word difficulty will play an important role in facilitating personalised learning in English reading and improve the efficiency of English reading.