ReaderBench
Mihai Dascălu, Larise Lucia Stavarache, Ștefan Trăușan-Matu, Philippe Dessus, Maryse Bianco, Danielle S. McNamara · 2015
The core of our ReaderBench software framework exposes a unified vision for predicting and assessing comprehension in both individual and collaborative learning scenarios. ReaderBench aims to improve both the quality and the classification of the analyzed documents by using an expanded range of criteria such as: morphology, semantics, discourse analysis with emphasis on polyphony and dialogism, thus providing reliable support for both tutors and students across a range of educational settings. ReaderBench uses a unitary cohesion-based representation of discourse applied into three major directions, all tightly connected by the underlying model and the Natural Language Processing (NLP) computations: reading strategies, textual complexity, and collaboration evaluation in Computer Supported Collaborative Learning (CSCL) conversations.