Improving reading comprehension step by step using Online-Boost text readability classification system

Lei La, Nan Wang, Dong-ping Zhou

Abstract: Online reading exercise becomes the universal tool for a wide variety of second language learning systems. Readability sorting is a key step to display suitable reading materials for the learners. Traditional text readability classification techniques cannot meet the request for online learning perfectly as they do not have real-time classification ability and cannot get the information of learners’ language levels. This paper presents a novel framework for online reading exercise which is based on the Online-Boost text readability classification algorithm. We first modified the multinomial Naïve Bayes model to give the reading materials initial readability. We then proposed an Online-Boost algorithm for the text readability update and learners’ reading comprehension evaluation according to the learners’ answers correct rate of the text. Finally, the system would deliver reading materials with different difficulties to testers with different levels of reading ability in real time. The experimental result reveals that the novel method has ideal ease of use and can significantly improve the performance of second language learners.