Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs

L’analyse sémantique automatique pour étudier les discussions visant la construction collaborative de connaissances

National audienceLe dialogue joue un grand rôle dans les apprentissages (Clarke, Resnick, Penstein Rosé, 2016) et plus largement dans le processus de construction de connaissances (Bereiter, 2002). Construire des connaissances, ce n’est donc pas seulement les acquérir mais plutôt considérer attentivement leurs relations, par une fréquentation assidue, pouvant être favorisée par des interactions sociales d’échange et de collaboration

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

Liftoff – ReaderBench introduces new online functionalities

International audienceNatural Language Processing (NLP) became a trending domain within recent years for many researches and companies due to its wide applicability and the new advances in technology. The aim of this paper is to introduce an updated version or our open-source NLP framework, ReaderBench (http://readerbench.com/), designed to support both students and tutors in multiple learning scenarios that encompass one or more of the following dimensions: Cohesion Network Analysis of discourse, textual complexity assessment, keywords’ extraction using Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and word2vec semantic models, as well as the analysis of online communities and discussions. The latest version of our ReaderBench framework (v4.1) includes: a) new features, Application Programing Interfaces (APIs) and visualizations (e.g., sociograms, analysis of interaction between participants inside a community), b) a new web interface written in Angular 6, and c) the integration of new technologies to increase performance (i.e., spaCy and AKKA), as well as modularity and ease of deployment (i.e., Artifactory and Maven modules). ReaderBench is a fully functional framework capable to enhance the quality of learning processes conducted in multiple languages (English, French, Romanian, Dutch, Spanish, and Italian), and covering both individual and collaborative assessments

Liftoff – ReaderBench introduces new online functionalities

International audienceNatural Language Processing (NLP) became a trending domain within recent years for many researches and companies due to its wide applicability and the new advances in technology. The aim of this paper is to introduce an updated version or our open-source NLP framework, ReaderBench (http://readerbench.com/), designed to support both students and tutors in multiple learning scenarios that encompass one or more of the following dimensions: Cohesion Network Analysis of discourse, textual complexity assessment, keywords’ extraction using Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and word2vec semantic models, as well as the analysis of online communities and discussions. The latest version of our ReaderBench framework (v4.1) includes: a) new features, Application Programing Interfaces (APIs) and visualizations (e.g., sociograms, analysis of interaction between participants inside a community), b) a new web interface written in Angular 6, and c) the integration of new technologies to increase performance (i.e., spaCy and AKKA), as well as modularity and ease of deployment (i.e., Artifactory and Maven modules). ReaderBench is a fully functional framework capable to enhance the quality of learning processes conducted in multiple languages (English, French, Romanian, Dutch, Spanish, and Italian), and covering both individual and collaborative assessments

Curricula customization with the readerbench Framework

Providing customized curricula tailored to learner's needs became a stringent problem while relating to the increasing number of people attending Massive Open Online Courses (MOOCs) and eLearning platforms because the same content is provided to all students. This study presents a Moodle plugin created on top of an eLearning course that enables curricula customization based on the learning needs of a high number of participants. With the help of the Mass Customization approach, two categories of attendees were identified in a previous research and imposed multiple filtering criteria, out of which the first one refers to participants� profession. The second criterion, topics of interest, allows learners to select keywords of interest from a predefined two-level word list, but also to enumerate their own terms using natural language. With the support of ReaderBench, an advanced Natural Language Processing framework, the most relevant lessons are retrieved in descending order of semantic relatedness. Third, an additional specific parameter allows participants to establish what kind of learning materials they require - i.e., theoretical and background oriented, practice and counseling documents, or guidelines. Our collection of documents is composed of lessons with a short description and their title, together with lists of pre- and post-requisite lessons. Our tool provides a comprehensive list of recommended lessons that best match the input criteria, corroborated with the list of related pre- and post-requisite lessons. Moreover, we provide information in terms of the duration of each lesson, as well as potential Continuous Medical Education points gained after finishing all selected lessons.<br/

Mass customization in continuing medical education: Automated extraction of E-Learning topics

To satisfy the individual learning needs of the high number of the Early Nutrition (EN) eAcademy participants, and to reduce development costs,the mass customization (MC) approach was applied. Key concepts of the learning needs, and corresponding learner subgroups with similar needs were extracted from learner-generated text using the natural language processing tool Reader‐Bench. Two collections of key concepts where built, which enabled EN experts to formulate topics for e-learning modules to be developed. Ongoing work will assess learner satisfaction and e-learning development costs, in order to evaluate the MC application in continuing medical education

Cohesion network analysis: customized curriculum management in Moodle

Learning Management Systems frequently act as platforms for online content which is usually structured hierarchically into modules and lessons to ease navigation. However, the volume of information may be overwhelming, or only part of the lessons may be relevant for an individual; thus, the need for customized curricula emerges. We introduce a Moodle plugin developed to help learners customize their curriculum to best fit their learning needs by relying on specific filtering criteria and semantic relatedness. For this experiment, a Moodle instance was created for doctors working in the field of nutrition in early life. The platform includes 78 lessons tackling a wide variety of topics, organized into five modules. Our plugin enables users to specify basic filtering criteria, including their field of expertise, topics of interest from a predefined taxonomy, or expected themes (e.g., background knowledge, practice &amp; counselling, or guidelines) for a preliminary pre-screening of lessons. In addition, learners can also provide a description in natural language of their learning interests. This text is compared with each lesson’s description using Cohesion Network Analysis, and lessons are selected above an experimentally set threshold. Our approach also takes into account prior knowledge requirements, and may suggest lessons for further reading. Overall, the plugin covers the management of the entire course lifecycle, namely: a) creating a customized curriculum; b) tracking the progress of completed lessons; c) generating completion certificates with corresponding CME points

Artificial intelligence moving serious gaming:Presenting reusable game AI components

This article provides a comprehensive overview of artificial intelligence (AI) for serious games. Reporting about the work of a European flagship project on serious game technologies, it presents a set of advanced game AI components that enable pedagogical affordances and that can be easily reused across a wide diversity of game engines and game platforms. Serious game AI functionalities include player modelling (real-time facial emotion recognition, automated difficulty adaptation, stealth assessment), natural language processing (sentiment analysis and essay scoring on free texts), and believable non-playing characters (emotional and socio-cultural, non-verbal bodily motion, and lip-synchronised speech), respectively. The reuse of these components enables game developers to develop high quality serious games at reduced costs and in shorter periods of time. All these components are open source software and can be freely downloaded from the newly launched portal at gamecomponents.eu. The components come with detailed installation manuals and tutorial videos. All components have been applied and validated in serious games that were tested with real end-users