caa a22 4500 60615986X CHVBK 20210128100627.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1770-2 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1770-2 Improving reading comprehension step by step using Online-Boost text readability classification system [Elektronische Daten] [Lei La, Nan Wang, Dong-ping Zhou] 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. The Natural Computing Applications Forum, 2014 Readability sorting nationallicence Text classification nationallicence Online learning nationallicence Reading comprehension nationallicence Boosting nationallicence Naïve Bayes nationallicence La Lei First Research Institute of Ministry of Public Security, Beijing, People's Republic of China aut Wang Nan First Research Institute of Ministry of Public Security, Beijing, People's Republic of China aut Zhou Dong-ping First Research Institute of Ministry of Public Security, Beijing, People's Republic of China aut Neural Computing and Applications Springer London 26/4(2015-05-01), 929-939 0941-0643 26:4<929 2015 26 521 https://doi.org/10.1007/s00521-014-1770-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1770-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- La Lei First Research Institute of Ministry of Public Security, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Wang Nan First Research Institute of Ministry of Public Security, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Zhou Dong-ping First Research Institute of Ministry of Public Security, Beijing, People's Republic of China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 929-939 0941-0643 26:4<929 2015 26 521