1. 'Please ReaderBench this text': A multi-dimensional textual complexity assessment framework
- Author
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Dascalu, Mihai, Crossley, Scott, Mcnamara, Danielle, Dessus, Philippe, Trausan-Matu, Stefan, 'Politehnica' Université de Bucarest, Roumanie, Romanian Academy of Sciences, University of Georgia [USA], Arizona State University [Tempe] (ASU), Laboratoire de Recherche sur les Apprentissages en Contexte (LaRAC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Education, Institute of Education Sciences - Grant R305A130124, USA, Department of Defense, Office of Naval Research - Grants N00014140343 and N000141712300, USA, S. D. Craig, European Project: 644187,H2020,H2020-ICT-2014-1,RAGE(2015), Dessus, Philippe, and Realising an Applied Gaming Eco-system - RAGE - - H20202015-02-01 - 2019-01-31 - 644187 - VALID
- Subjects
learning analytics ,[INFO.EIAH] Computer Science [cs]/Technology for Human Learning ,[SHS.EDU]Humanities and Social Sciences/Education ,[SHS.EDU] Humanities and Social Sciences/Education ,comprehension modeling ,automated essay scoring ,[INFO.EIAH]Computer Science [cs]/Technology for Human Learning ,data analytics ,Natural Language Processing - Abstract
International audience; A critical task for tutors is to provide learners with suitable reading materials in terms of difficulty. The challenge of this endeavor is increased by students' individual variability and the multiple levels in which complexity can vary, thus arguing for the necessity of automated systems to support teachers. This chapter describes ReaderBench, an open-source multi-dimensional and multilingual system that uses advanced Natural Language Processing techniques to assess textual complexity at multiple levels including surface-based, syntax, semantics and discourse structure. In contrast to other existing approaches, ReaderBench is centered on cohesion and makes extensive usage of two complementary models, i.e., Cohesion Network Analysis and the polyphonic model inspired from dialogism. The first model provides an in-depth view of discourse in terms of cohesive links, whereas the second one highlights interactions between points of view spanning throughout the discourse. In order to argue for its wide applicability and extensibility, two studies are introduced. The first study investigates the degree to which ReaderBench textual complexity indices differentiate between high and low cohesion texts. The ReaderBench indices led to a higher classification accuracy than those included in prior studies using Coh-Metrix and TAACO. In the second study, ReaderBench indices are used to predict the difficulty of a set of various texts. Although the high number of predictive indices (50 plus) accounted for less variance than previous studies, they make valuable contributions to our understanding of text due to their wide coverage.
- Published
- 2018