scholarly journals The Application of Affective Computing Technology to E-Learning

2014 ◽  
pp. 109-128
Author(s):  
Nik Thompson ◽  
Tanya Jane McGill
Keyword(s):  
Affective Computing ◽  
Physiological State ◽  
Affective State ◽  
Significant Proportion ◽  
Human Interaction ◽  
Human Communication ◽  
Tutoring Systems ◽  
User Input ◽  
E Learning ◽  
New Generation

This chapter discusses the domain of affective computing and reviews the area of affective tutoring systems: e-learning applications that possess the ability to detect and appropriately respond to the affective state of the learner. A significant proportion of human communication is non-verbal or implicit, and the communication of affective state provides valuable context and insights. Computers are for all intents and purposes blind to this form of communication, creating what has been described as an “affective gap.” Affective computing aims to eliminate this gap and to foster the development of a new generation of computer interfaces that emulate a more natural human-human interaction paradigm. The domain of learning is considered to be of particular note due to the complex interplay between emotions and learning. This is discussed in this chapter along with the need for new theories of learning that incorporate affect. Next, the more commonly applicable means for inferring affective state are identified and discussed. These can be broadly categorized into methods that involve the user’s input and methods that acquire the information independent of any user input. This latter category is of interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions, and physiological state. The chapter concludes with a review of prominent affective tutoring systems in current research and promotes future directions for e-learning that capitalize on the strengths of affective computing.

Affective Tutoring Systems

2012 ◽  
Vol 8 (4) ◽  
pp. 75-89 ◽  
Author(s):  
Nik Thompson ◽  
Tanya Jane McGill
Keyword(s):  
Affective Computing ◽  
Physiological State ◽  
Affective State ◽  
Complex Interplay ◽  
Future Directions ◽  
Tutoring Systems ◽  
User Input ◽  
Face To Face ◽  
E Learning ◽  
Potential Benefits

This paper introduces the field of affective computing, and the benefits that can be realized by enhancing e-learning applications with the ability to detect and respond to emotions experienced by the learner. Affective computing has potential benefits for all areas of computing where the computer replaces or mediates face to face communication. The particular relevance of affective computing to e-learning, due to the complex interplay between emotions and the learning process, is considered along with the need for new theories of learning that incorporate affect. Some of the potential means for inferring users’ affective state are also reviewed. These can be broadly categorized into methods that involve the user’s input, and methods that acquire the information independent of any user input. This latter category is of particular interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions or physiological state. The paper concludes with a review of prominent affective tutoring systems and promotes future directions for e-learning that capitalize on the strengths of affective computing.

Replicating Human Interaction to Support E-Learning

2006 ◽  
pp. 494-503
Author(s):  
William A. Janvier ◽  
Claude Ghaoui
Keyword(s):  
Learning Styles ◽  
Text Messaging ◽  
Learning Experience ◽  
Human Interaction ◽  
Learning Tools ◽  
Tutoring Systems ◽  
Language Patterns ◽  
E Learning ◽  
Communication Preference ◽  
The Way

HCI-related subjects need to be considered to make e-learning more effective; examples of such subjects are: psychology, sociology, cognitive science, ergonomics, computer science, software engineering, users, design, usability evaluation, learning styles, teaching styles, communication preference, personality types, and neuro-linguistic programming language patterns. This article discusses the way some components of HI can be introduced to increase the effectiveness of e-learning by using an intuitive interactive e-learning tool that incorporates communication preference (CP), specific learning styles (LS), neurolinguistic programming (NLP) language patterns, and subliminal text messaging. The article starts by looking at the current state of distance learning tools (DLTs), intelligent tutoring systems (ITS) and “the way we learn”. It then discusses HI and shows how this was implemented to enhance the learning experience.

Emotional Design Tutoring System Based on Multimodal Affective Computing Techniques

2018 ◽  
Vol 16 (1) ◽  
pp. 103-117 ◽  
Author(s):  
Cheng-Hung Wang ◽  
Hao-Chiang Koong Lin
Keyword(s):  
Affective Computing ◽  
User Interaction ◽  
Learning Effects ◽  
Emotional Design ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Face To Face ◽  
Learning Satisfaction ◽  
Positive Attitudes ◽  
E Learning

In a traditional class, the role of the teacher is to teach and that of the students is to learn. However, the constant and rapid technological advancements have transformed education in numerous ways. For instance, in addition to traditional, face to face teaching, E-learning is now possible. Nevertheless, face to face teaching is unavailable in distance education, preventing the teacher from understanding the student's learning emotions and states; hence, a system can be adopted to collect information on students' learning emotions, thereby compiling data to analyze their learning progresses. Hence, this study established an emotional design tutoring system (EDTS) and investigated whether this system influences user interaction satisfaction and elevates learning motivation. This study determined that the learners' perception of affective tutoring systems fostered positive attitudes toward learning and thereby promoted learning effects. The experimental results offer teachers and learners an efficient technique for boosting students' learning effects and learning satisfaction. In the future, affective computing is expected to be widely used in teaching. This can enable students to enjoy learning in a multilearning environment; thus, they can exhibit higher learning satisfaction and gain considerable learning effects.

scholarly journals Affective Medicine

2010 ◽  
Vol 49 (03) ◽  
pp. 207-218 ◽  
Author(s):  
A. Luneski ◽  
E. Konstantinidis ◽  
P. D. Bamidis
Keyword(s):  
Medical Informatics ◽  
Affective Computing ◽  
Virtual Communities ◽  
State Of The Art ◽  
Affective State ◽  
Home Healthcare ◽  
Medical Community ◽  
Affective Communication ◽  
E Learning ◽  
Application Fields

Summary 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, Am I, 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.

E-Learning with Affective Tutoring Systems

2011 ◽  
pp. 129-140
Author(s):  
Abdolhossein Sarrafzadeh ◽  
Jamshid Shanbehzadeh ◽  
Scott Overmyer
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Affective State ◽  
Learning Systems ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Effective Current ◽  
Animated Agent ◽  
E Learning ◽  
K 12

E-learning has attracted a great deal of interest in educational circles from K-12 to universities. A question that is often rightly asked is how effective current e-learning systems are. It is argued that there is little individualization of instruction by adapting to the pedagogical needs of each learner in current e-learning systems. Intelligent tutoring systems have tried to fill this gap but even they fail to compete with human one-to-one tutoring. This paper presents Affective Tutoring Systems which are e-learning systems capable of detecting learners’ affective state and reacting to it through a life like agent called Eve. This paper presents an Affective Tutoring System in the domain of mathematics and the research that led to its development. It also presents the findings from the study and testing of the system indicating that the animated agent Eve carried a persona effect.

Emotional Models

2020 ◽  
Vol 11 (2) ◽  
pp. 1-18
Author(s):  
Rana Fathalla
Keyword(s):  
Affective Computing ◽  
Computational Models ◽  
Human Interaction ◽  
Emotion Classification ◽  
Tutoring Systems ◽  
Emotion Model ◽  
Emotion Generation ◽  
Emotion Representation ◽  
Emotion Modeling ◽  
Componential Models

Emotion modeling has gained attention for almost two decades now due to the rapid growth of affective computing (AC). AC aims to detect and respond to the end-user's emotions by devices and computers. Despite the hard efforts being directed to emotion modeling with numerous tries to build different models of emotions, emotion modeling remains an art with a lack of consistency and clarity regarding the exact meaning of emotion modeling. This review deconstructs the vagueness of the term ‘emotion modeling' by discussing the various types and categories of emotion modeling, including computational models and its categories—emotion generation and emotion effects—and emotion representation models and its categories—categorical, dimensional, and componential models. This review deals with applications associated with each type of emotion model including artificial intelligence and robotics architecture, computer-human interaction applications of the computational models, and emotion classification and affect-aware applications such as video games and tutoring systems applications of emotion representation models.

Live-action mass-casualty training and virtual world training

2016 ◽  
Vol 60 (1) ◽  
pp. 2103-2107 ◽  
Author(s):  
Keith T. Shubeck ◽  
Scotty D. Craig ◽  
Xiangen Hu
Keyword(s):  
Virtual World ◽  
Domain Knowledge ◽  
Mass Casualty ◽  
Learning Technologies ◽  
Learning Needs ◽  
Web Based ◽  
Tutoring Systems ◽  
Aeromedical Evacuation ◽  
User Input ◽  
Live Action

Live-action training simulations with expert facilitators are considered by many to be the gold-standard in training environments. However, these training environments are expensive, provide many logistical challenges, and may not address the individual’s learning needs. Fortunately, advances in distance-based learning technologies have provided the foundation for inexpensive and effective learning environments that can simultaneously train and educate students on a much broader scale than live-action training environments. Specifically, intelligent tutoring systems (ITSs) have been proven to be very effective in improving learning outcomes. The Virtual Civilian Aeromedical Evacuation Sustainment Training (VCAEST) interface takes advantage of both of these technologies by enhancing a virtual world with a web-based ITS, AutoTutor LITE (Learning in Interactive Training Environments). AutoTutor LITE acts as a facilitator in the virtual world by providing just-in-time feedback, presenting essential domain knowledge, and by utilizing tutoring dialogues that automatically assess user input. This paper will discuss the results of an experimental evaluation of the VCAEST environment compared to an expert-led live-action training simulation.

scholarly journals Affective Educational Games and the Evolving Teaching Experience

2011 ◽  
pp. 206-228
Author(s):  
Karla Muñoz ◽  
Paul Mc Kevitt ◽  
Tom Lunney ◽  
Julieta Noguez ◽  
Luis Neri
Keyword(s):  
Bayesian Networks ◽  
Educational Games ◽  
Affective State ◽  
Computer Game ◽  
Teaching Experience ◽  
Learning Goals ◽  
Game Based Learning ◽  
Student Model ◽  
Tutoring Systems ◽  
Control Value

Teaching methods must adapt to learners’ expectations. Computer game-based learning environments enable learning through experimentation and are inherently motivational. However, for identifying when learners achieve learning goals and providing suitable feedback, Intelligent Tutoring Systems must be used. Recognizing the learner’s affective state enables educational games to improve the learner’s experience or to distinguish relevant emotions. This chapter discusses the creation of an affective student model that infers the learner’s emotions from cognitive and motivational variables through observable behavior. The control-value theory of ‘achievement emotions’ provides a basis for this work. A Probabilistic Relational Models (PRMs) approach for affective student modeling, which is based on Dynamic Bayesian Networks, is discussed. The approach is tested through a prototyping study based on Wizard-of-Oz experiments and preliminary results are presented. The affective student model will be incorporated into PlayPhysics, an emotional game-based learning environment for teaching Physics. PRMs facilitate the design of student models with Bayesian Networks. The effectiveness of PlayPhysics will be evaluated by comparing the students’ learning gains and learning efficiencies.

scholarly journals Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

2021 ◽  
Vol 4 (2) ◽  
pp. 55-76
Author(s):  
Dan Oyuga Anne ◽  
Elizaphan Maina
Keyword(s):  
Learning Style ◽  
Affective State ◽  
Learner Characteristics ◽  
Q Learning ◽  
Model Free ◽  
Learning Platform ◽  
Learning Path ◽  
Multiple Characteristics ◽  
E Learning ◽  
Extraction Model

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

scholarly journals Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6438
Author(s):  
Chiara Filippini ◽  
David Perpetuini ◽  
Daniela Cardone ◽  
Arcangelo Merla
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Affective Computing ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Expression Recognition ◽  
Robot Interaction ◽  
Interaction Field ◽  
Nao Robot

An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s.

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