With the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tightly intertwined activities of comprehension of reading materials and of collaboration among peers has grown significantly. Whereas a shallow or surface analysis is easily achievable, a deeper understanding of the discourse is required, extended by meta-cognitive information available from multiple sources as self-explanations. In this context, we use a polyphonic model of discourse derived from Bakhtin’s work as a paradigm for analyzing CSCL conversations, as well as cohesion graph building designed for creating an underlying discourse structure. This enables us to address both general texts and conversations and to incorporate comprehension and collaboration specific activities in a unique framework. As specificity of the analysis, in terms of individual learning we have focused on the identification of reading strategies and on providing a multi-dimensional textual complexity model integrating surface, word specific, morphology, syntax and semantic factors. Complementarily, the collaborative learning dimension is centered on the evaluation of participants’ involvement, as well as on collaboration assessment through the use of two computational models: a polyphonic model, defined in terms of voice inter-animation, and a specific social knowledge-building model, derived from the specially designed cohesion graph corroborated with a proposed utterance scoring mechanism. Our approach integrates advanced Natural Language Processing techniques and is focused on providing a qualitative estimation of the learning process. Therefore, two tightly coupled perspectives are taken into consideration: comprehension on one hand is centered on knowledge-building, self-explanations from which multiple reading strategies can be identified, whereas collaboration, on the other, can be seen as social involvement, ideas or voices generation, intertwining and inter-animation in a given context. Various cognitive validations for all our automated evaluation systems have been conducted and scenarios including the use of ReaderBench, our most advanced system, in different educational contexts have been built. One of the most important goals of our model is to enhance understanding as a “mediator of learning” by providing automated feedback to both learners and teachers or tutors. The main benefits are its flexibility, extensibility and nevertheless specificity for covering multiple stages, starting from reading classroom materials, to discussing on specific topics in a collaborative manner, and finishing the feedback loop by verbalizing metacognitive thoughts in order to obtain a clear perspective over one’s comprehension level and appropriate feedback about the collaborative learning processes.
Analysing discourse and text complexity for learning and collaborating