Abstract-Research efforts in terms of automatic textual complexity analysis are mainly focused on English vocabulary and few adaptations exist for other languages. Starting from a solid base in terms of discourse analysis and existing textual complexity assessment model for English, we introduce a French model trained on 200 documents extracted from school manuals pre-classified into five complexity classes. The underlying textual complexity metrics include surface, syntactic, morphological, semantic and discourse specific factors that are afterwards combined through the use of Support Vector Machines. In the end, each factor is correlated to pupil comprehension metrics scores, spanning throughout multiple classes, therefore creating a clearer perspective in terms of measurements impacting the perceived difficulty of a given text. In addition to purely quantitative surface factors, specific parts of speech and cohesion have proven to be reliable predictors of learners' comprehension level, creating nevertheless a strong background for building dependable French textual complexity models.
Abstract.ReaderBench is an automated software framework designed to support both students and tutors by making use of text mining techniques, advanced natural language processing, and social network analysis tools. ReaderBench is centered on comprehension prediction and assessment based on a cohesion-based representation of the discourse applied on different sources (e.g., textual materials, behavior tracks, metacognitive explanations, Computer Supported Collaborative Learning -CSCL -conversations). Therefore, ReaderBench can act as a Personal Learning Environment (PLE) which incorporates both individual and collaborative assessments. Besides the a priori evaluation of textual materials' complexity presented to learners, our system supports the identification of reading strategies evident within the learners' self-explanations or summaries. Moreover, ReaderBench integrates a dedicated cohesion-based module to assess participation and collaboration in CSCL conversations.
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