Vers un environnement-tuteur d'apprentissage dialogique

Cet article présente un environnement-tuteur d'apprentissage pouvant s'insérer dans une plate-forme de FOAD permettant à des étudiants de prendre connaissance et de réviser des contenus de cours. Cet environnement puise son originalité dans le fait qu'il allie une approche cognitive et des références à l'apprentissage dialogique pour permettre : — de fournir aux apprenants les cours les plus pertinents à étudier étant donné leur compréhension du cours ; — de délivrer des feedback sur la compréhension d'un cours et le contenu de résumés produits sur ce même cours ; — de renvoyer des feedback sur les fils de discussions menées dans des chats

Identifying the Structure of CSCL Conversations Using String Kernels

Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow by teachers due to the mixture of multiple and many times concurrent discussion threads, with different interaction patterns between participants. Automated guidance can be provided with the help of Natural Language Processing techniques that target the identification of topic mixtures and of semantic links between utterances in order to adequately observe the debate and continuation of ideas. This paper introduces a method for discovering such semantic links embedded within chat conversations using string kernels, word embeddings, and neural networks. Our approach was validated on two datasets and obtained state-of-the-art results on both. Trained on a relatively small set of conversations, our models relying on string kernels are very effective for detecting such semantic links with a matching accuracy larger than 50% and represent a better alternative to complex deep neural networks, frequently employed in various Natural Language Processing tasks where large datasets are available

Assessing writing and collaboration in learning: Methodological issues

International audienceWe describe the main tasks students usually complete when working in an e- learning platform, across five mean features that have to be taken into account in research efforts (writing-based activities, individual/collective level, knowledge/ pedagogy orientation, feedback, multiple stakeholders account). Ways to analyse and assist these tasks by (semi)-automatic assessments using NLP techniques is discussed. Two services aiming to assist writing-based tasks are presented along with their first validation

Visualization 1: Ignition of an automobile engine by high-peak power Nd:YAG/Cr⁴⁺:YAG laser-spark devices

A short movie, .mp4 format, associated to Fig. 6c. Originally published in Optics Express on 28 December 2015 (oe-23-26-33028

Deliverable 5.2 LTfLL – Learning support and feedback

This report presents Version 1 of the support and feedback services (delivering recommendations based on interaction analysis and on students’ textual production) that can be integrated within an e-learning environment. Further steps toward the implementation of Version 2 of these services and their future integration with all the LTfLL services are also suggested