Improving Student’s Emotional Intelligence By Mathematics Learning

This paper aims to provide a description of realistic mathematics education in improving emotional intelligence. Mathematics is a tool that can be developed to foster thinking (reasoning) and attitudes (emotions) (Nelissen, 2007). Emotional intelligence is the ability to feel, understand and effectively apply the power and emotional sensitivity as an energy source and as a motivator. The models that emerged from students' mathematical activity can promote interaction in class, thus leading to the level of mathematical thinking and higher learning meaningful democracy. Thus, learning mathematics is a realistic learning actively involves students both physically and mentally (student centered learning), and be democratic, so as to have a better profile in the critical thinking skills and emotional intelligence of students. Keywords: emotional intelligence, learning, mathematics, constructive, interactive, reflective, realisti

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

The emotional weight of "I love you" in multilinguals' languages

The present paper considers the perceived emotional weight of the phrase I love you in multilinguals’ different languages. The sample consists of 1459 adult multilinguals speaking a total of 77 different first languages. They filled out an on-line questionnaire with open and closed questions linked to language behavior and emotions. Feedback on the open question related to perceived emotional weight of the phrase I love you in the multilinguals’ different languages was recoded in three categories: it being strongest in (1) the first language (L1), (2) the first language and a foreign language, and (3) a foreign language (LX). A majority of speakers felt I love you was strongest in their L1. Participants offered various explanations for their perception. Statistical analyses revealed that the perception of weight of the phrase I love you was associated with self-perceived language dominance, context of acquisition of the L2, age of onset of learning the L2, degree of socialization in the L2, nature of the network of interlocutors in the L2, and self-perceived oral proficiency in the L2

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

RRL: A Rich Representation Language for the Description of Agent Behaviour in NECA

In this paper, we describe the Rich Representation Language (RRL) which is used in the NECA system. The NECA system generates interactions between two or more animated characters. The RRL is a formal framework for representing the information that is exchanged at the interfaces between the various NECA system modules