Gifted students have a higher capabilities of understanding and learning. They are characterized by a high level of attention and a high performance in the classroom. Gifted children are defined in this paper as children who have a performance higher than the average group (59.64%). In order to predict gifted students from normal students, we conducted an experiment where 17 pupils have voluntarily participated in this study. We collected different types of data (gender, age, performance, initial average in math and EEG mental states) in a web platform to learn mathematics called NetMath. Participants were invited to respond to top-level exercises on the four basic operations in decimals. We trained different machine learning algorithms to predict gifted students. Our first results show that the decision tree could predict gifted students with an accuracy of 76.88%. Using J48 trees, we noticed also that two relevant features could determine gifted children: the relaxation extracted from EEG headset and the characteristic of strong student. A strong student is defined as a student who obtained a mean higher than the group’s mean in the first step evaluation in class.

Traditionally, the study of computational linguistics (CL) has been performed by computer scientists specializing in the application of computers to the processing of a natural language. Today, computational linguists often work as members of interdisciplinary teams that include linguists (specifically trained in linguistics), language experts (persons with some level of ability in the languages relevant to a given project), and computer scientists. CL must become more connected to the cognitive sciences through the development of cognitive semantic theories. CL is connected to AI through the development of methods and algorithms for all aspects of language analysis and their computer implementation. We can see language analysis divided into two parts: theoretical analysis and application.

This allows for supporting more adequate and accurate representations of learners, their learning goals, learning material and contexts of its use, as well as more efficient access and navigation through learning resources. The goal is to advance intelligent educational systems, so as to achieve improved e-learning efficiency, flexibility and adaptation for single users and communities of users (learners, instructors, courseware authors, and others). The special track follows the workshop series Ontologies and Semantic Web for e-Learning, which was conducted successfully from 2002-2009 at a number of different conferences. The goals of this track are to discuss the current state-of-the-art in using ontologies and semantic web technologies in e-learning applications; and to attract the interest of the related research communities to the problems in the educational social semantic web and serve as an international platform for knowledge exchange and cooperation between researchers. This special track will be of interest to researchers interested in using ontologies, semantic web and social semantic web technologies in web-based educational systems, distributed hypermedia and open hypermedia systems, as well as in web intelligence and semantic web and social semantic web engineering.

In this paper we propose the CTS (Concious Tutoring System) technology, a biologically plausible cognitive agent based on human brain functions.This agent is capable of learning and remembering events and any related information such as corresponding procedures, stimuli and their emotional valences. Our proposed episodic memory and episodic learning mechanism are closer to the current multiple-trace theory in neuroscience, because they are inspired by it [5] contrary to other mechanisms that are incorporated in cognitive agents. This is because in our model emotions play a role in the encoding and remembering of events. This allows the agent to improve its behavior by remembering previously selected behaviors which are influenced by its emotional mechanism. Moreover, the architecture incorporates a realistic memory consolidation process based on a data mining algorithm.