ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

International audienceIn this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides in- depth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests

Analyzing the Semantic Relatedness of Paper Abstracts: An Application to the Educational Research Field

International audienceEach domain, along with its knowledge base, changes over time and every timeframe is centered on specific topics that emerge from different ongoing research projects. As searching for relevant resources is a time-consuming process, the automatic extraction of the most important and relevant articles from a domain becomes essential in supporting researchers in their day-today activities. The proposed analysis extends other previous researches focused on extracting co-citations between the papers, with the purpose of comparing their overall importance within the domain from a semantic perspective. Our method focuses on the semantic analysis of paper abstracts by using Natural Language Processing (NLP) techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or specific ontology distances, i.e., WordNet. Moreover, the defined mechanisms are enforced on two different subdomains from the corpora generated around the keywords " e-learning " and " computer ". Graph visual representations are used to highlight the keywords of each subdomain, links among concepts and between articles, as well as specific document similarity views, or scores reflecting the keyword-abstract overlaps. In the end, conclusions and future improvements are presented, emphasizing nevertheless the key elements of our research support framework

Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy

International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure

Predicting Comprehension from Students’ Summaries

International audienceComprehension among young students represents a key component of their formation throughout the learning process. Moreover, scaffolding students as they learn to coherently link information, while organically construct- ing a solid knowledge base, is crucial to students’ development, but requires regular assessment and progress tracking. To this end, our aim is to provide an automated solution for analyzing and predicting students’ comprehension levels by extracting a combination of reading strategies and textual complexity factors from students’ summaries. Building upon previous research and enhancing it by incorporating new heuristics and factors, Support Vector Machine classification models were used to validate our assumptions that automatically identified reading strategies, together with textual complexity indices applied on students’ summaries, represent reliable estimators of comprehension

Dialogism: A Framework for CSCL and a Signature of Collaboration

International audienceAs Computer Supported Collaborative Learning (CSCL) gains a broader usage in multiple educational scenarios facilitated by the use of technology, the need for automated tools capable of detecting and stimulating collaboration increases. We propose a computational model using ReaderBench that assesses collaboration from a dialogical perspective. Accordingly, collaboration emerges from the intertwining of different points of view or, more specifically, from the inter-animation of voices pertaining to different speakers. Collaboration is determined from the intertwining or overlap of voices emitted by different participants throughout the ongoing conversation. This study presents a validation of this model consisting of a comparison between the output of our system and human evaluations of 10 chat conversations, selected from a corpus of more than 100 chats, in which Computer Science students debated on the advantages and disadvantages of CSCL technologies (e.g., chat, blog, wiki, forum, or Google Wave). The human evaluations of the degree of collaboration between the participants and the automated scores showed good overlap as measured by precision, recall, and F1 scores. Our overarching conclusion is that dialogism derived from the overlapping of voices can be perceived as a signature for collaboration

Are Automatically Identified Reading Strategies Reliable Predictors of Comprehension?

International audienceIn order to build coherent textual representations, readers use cognitive procedures and processes referred to as reading strategies; these specific procedures can be elicited through self-explanations in order to improve understanding. In addition, when faced with comprehension difficulties, learners can invoke regulation processes, also part of reading strategies, for facilitating the understanding of a text. Starting from these observations, several automated techniques have been developed in order to support learners in terms of efficiency and focus on the actual comprehension of the learning material. Our aim is to go one step further and determine how automatically identified reading strategies employed by pupils with age between 8 and 11 years can be related to their overall level of understanding. Multiple classifiers based on Support Vector Machines are built using the strategies' identification heuristics in order to create an integrated model capable of predicting the learner's comprehension level

Fake news detection and analysis

The evolution of technology has led to the development of environments that allow instantaneous communication and dissemination of information. As a result, false news, article manipulation, lack of trust in media and information bubbles have become high-impact issues. In this context, the need for automatic tools that can classify the content as reliable or not and that can create a trustworthy environment is continually increasing. Current solutions do not entirely solve this problem as the degree of difficulty of the task is high and dependent on factors such as type of language, type of news or subject volatility. The main objective of this thesis is the exploration of this crucial problem of Natural Language Processing, namely false content detection and of how it can be solved as a classification problem with automatic learning. A linguistic approach is taken, experimenting with different types of features and models to build accurate fake news detectors. The experiments are structured in the following three main steps: text pre-processing, feature extraction and classification itself. In addition, they are conducted on a real-world dataset, LIAR, to offer a good overview of which model best overcomes day-to-day situations. Two approaches are chosen: multi-class and binary classification. In both cases, we prove that out of all the experiments, a simple feed-forward network combined with fine-tuned DistilBERT embeddings reports the highest accuracy - 27.30% on 6-labels classification and 63.61% on 2-labels classification. These results emphasize that transfer learning bring important improvements in this task. In addition, we demonstrate that classic machine learning algorithms like Decision Tree, Naïve Bayes, and Support Vector Machine act similar with the state-of-the-art solutions, even performing better than some recurrent neural networks like LSTM or BiLSTM. This clearly confirms that more complex solutions do not guarantee higher performance. Regarding features, we confirm that there is a connection between the degree of veracity of a text and the frequency of terms, more powerful than their position or order. Yet, context prove to be the most powerful aspect in the characteristic extraction process. Also, indices that describe the author's style must be carefully selected to provide relevant information