Analysing discourse and text complexity for learning and collaborating
type: Dataset
contact point: Organization
  fn: CCSD
keyword: shseduhumanities-and-social-scienceseducation
keyword: traitement-automatique-de-la-langue
keyword: complexite
keyword: computer-supported-collaborative-learning
keyword: complexity
keyword: discourse-analysis
keyword: natural-language-processing
keyword: apprentissage-collaboratif-assiste-par-ordinateur
keyword: comprehension-de-texte
keyword: theses
keyword: textual-comprehension
keyword: analyse-du-discours
keyword: infoeu-reposemanticsdoctoralthesis
landing page: https://theses.hal.science/tel-00978420
description: 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 se…
identifier: NNT: 2013GRENH004
issued: 2026-05-05T14:54:03.362894
language: fr
modified: 2026-05-05T14:54:03.362899

test_moissonnage_selune
type: Agent

Analysing discourse and text complexity for learning and collaborating
type: Distribution
access URL: https://theses.hal.science/tel-00978420
format: HTML
issued: 2026-05-05T14:54:03.377902
modified: 2026-05-05T14:54:03.344880