scholarly journals Affective Computing in E-learning

E-learning ◽  
10.5772/7780 ◽  
2010 ◽  
Author(s):  
Hongfei Lin ◽  
Fengming Pan ◽  
Yuxuan Wang ◽  
Shaohua Lv ◽  
Shichang Su
Keyword(s):  
Affective Computing ◽  
E Learning

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.

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.

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.

The Role of Affect in an Agent-Based Collaborative E-Learning System Used for Engineering Education

2009 ◽  
pp. 510-528
Author(s):  
Mohamed Ben Ammar ◽  
Mahmoud Neji ◽  
Adel M. Alimi
Keyword(s):  
Facial Expressions ◽  
Affective Computing ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Systems ◽  
Learning System ◽  
Agent Based ◽  
Affective Behaviors ◽  
E Learning

Affective computing is a new artificial intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways. This chapter represents an implemented framework, which integrates this new area with an intelligent tutoring system. The authors argue that tutor agents providing socially appropriate affective behaviors would provide a new dimension for collaborative learning systems. The main goal is to analyse learner facial expressions and show how affective computing could contribute to learning interactions, both by recognizing learner emotions during learning sessions and by responding appropriately.

Experiential Learning in the Myself Project

2011 ◽  
pp. 360-372 ◽  
Author(s):  
Luigi Anolli ◽  
Fabrizia Mantovani ◽  
Massimo Balestra ◽  
Piet Kommers ◽  
Odile Robotti ◽  
...  
Keyword(s):  
Experiential Learning ◽  
Affective Computing ◽  
Soft Skills ◽  
Skills Training ◽  
Open Questions ◽  
Critical Issues ◽  
E Learning ◽  
Project Objectives ◽  
Interactive Simulations ◽  
And Training

New trends in information technology are strongly influencing and shaping the growth of e-learning, and progressively resolving a number of critical issues currently limiting its dissemination to wider populations. The main goal of this chapter is to outline the MySelf Project, which aims to expand the potential of e-learning through the implementation of Affective Computing and training in soft skills. The chapter contents are therefore divided into two main sections: firstly, implementation of affective computing in the Myself project through the design and development of a 3D virtual tutor and research on possible implementations of multimodal recognition of user emotions; secondly, the development of 3D interactive simulations for soft skills training. Each section sets out the state of the art for the areas covered, outlines the Myself project objectives and possible operative applications, describes the work carried out to date, and discusses critical issues, open questions and future directions for the project.

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