Michalis Feidakis

Dr. Michalis Feidakis

University of West Attica, Athens, Attikí, Greece
orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 November 2011
Verified
Conference Paper
Closed access
MFMichalis Feidakis
Michalis Feidakis
Thanasis DaradoumisThanasis Daradoumis
Thanasis Daradoumis
Santi CaballéSanti Caballé
Santi Caballé
The consideration of emotion in learning has been in the research agenda for quite long time and literature is offering a variety of studies that are evaluating the role of emotions in various learning contexts (class, test and exams, during studying at home, etc). In the current paper we first appose a critical review of prominent emotion taxonomies, affect detection and feedback strategies and then we propose the design of a system that is endowed with students' emotion aware capabilities. An emotion taxonomy that originates from the basic emotions of fear and affection is put forward. Social-Emotional Learning Theory and Practice can be exploited to feed the necessary theoretical background that is lacking of empirical data.

1 November 2011
Verified
Conference Paper
Closed access
MFMichalis Feidakis
Michalis Feidakis
Thanasis DaradoumisThanasis Daradoumis
Thanasis Daradoumis
Santi CaballéSanti Caballé
Santi Caballé
Affective or Emotion oriented computing constitutes an emerging research field that is still in its early stages. The lack of empirical results together with the complexity that attributes emotions, subjects research to a diversity of theories, models and tools. In the current paper we present a critical review of the state of the art on emotion measurement models, methods and tools and we suggest some informal rules towards their realistic use in education settings.


2 May 2022 in Computers
Verified
Journal Article
Open access
PDF file
IVIro Athina Valagkouti
Iro Athina Valagkouti
Christos TroussasChristos Troussas
Christos Troussas
Akrivi KrouskaAkrivi Krouska
Akrivi Krouska
MFMichalis Feidakis
Michalis Feidakis
Cleo SgouropoulouCleo Sgouropoulou
Cleo Sgouropoulou
Affective computing can be implemented across many fields in order to provide a unique experience by tailoring services and products according to each person’s needs and interests. More specifically, digital learning and robotics in education can benefit from affective computing with a redesign of the curriculum’s contents based on students’ emotions during teaching. This key feature is observed during traditional learning methods, and robot tutors are adapting to it gradually. Following this trend, this work focused on creating a game that aims to raise environmental awareness by using the social robot NAO as a conversation agent. This quiz-like game supports emotion recognition with DeepFace, allowing users to review their answers if a negative emotion is detected. A version of this game was tested during real-life circumstances and produced favorable results, both for emotion analysis and overall user enjoyment.

27 April 2014 in International Journal of Emerging Technologies in Learning (iJET)
Verified
Journal Article
Open access
PDF file
MFMichalis Feidakis
Michalis Feidakis
Thanasis DaradoumisThanasis Daradoumis
Thanasis Daradoumis
Santi CaballéSanti Caballé
Santi Caballé
JCJordi Conesa
Jordi Conesa
In this work we investigate the importance of emotion awareness in e-learning environments, with emphasis to Computer Supported Collaborative Learning (CSCL) activities. Our presented solution involves a new conceptual model of emotions of interest in e-learning context. In the basis of this model, a computational model has been implemented employing self-report of emotions, affective feedback and effective emotion visualizations. Both models have been tested in real education settings, contributing to the research agenda.

21 June 2023 in Applied Sciences
Verified
Journal Article
Open access
PDF file
Alvaro GutiérrezAlvaro Gutiérrez
Alvaro Gutiérrez
PHPatricia Blanco Hermo
Patricia Blanco Hermo
VVerónica Ruiz Bejerano
Verónica Ruiz Bejerano
CCChristos Chatzigeorgiou
Christos Chatzigeorgiou
XOXabier Oregui
Xabier Oregui
FAFederico Alvarez
Federico Alvarez
During the last decade, new technological tools have emerged to provide first responders with augmented senses in emergency and disaster situations. Some of these tools focus on providing extra information about their surroundings. However, despite augmenting first responders’ capabilities, the quantity and the way that this information is presented can affect their cognitive load. This manuscript presents an integrated framework that allows real-time biosignals monitoring to analyze physical constants and correlate them with subjective cognitive load tests. Biosignals monitoring allows alarms to be raised related to the physical status while cognitive load values will allow modulation of the amount of information that can be assimilated by the first responder in operation. In-lab and practice experimental tests have been conducted to create a fully functional framework. During the technical validation, a strong dispersion of subjective cognitive load by means of NASA-TLX questionnaires has been found between participants. Nonetheless, the developed framework allows extraction of relationships between biosignals and cognitive load, with special attention to the respiration rate and eye movements.