Computer-Based Learning Environments with Emotional Agents

Computer-Based Learning Environments with Emotional Agents

Dorel Gorga (University of Geneva, Switzerland) and Daniel K. Schneider (University of Geneva, Switzerland)
DOI: 10.4018/978-1-60566-354-8.ch021
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The purpose of this contribution is to discuss conceptual issues and challenges related to the integration of emotional agents in the design of computer-based learning environments and to propose a framework for the discussion of future research. We review some emotion theories and computational models that have been developed in cognitive science and Artificial Intelligence (AI). We then will discuss some basic principles pertaining to motivation and emotion in instructional design. Grounded on these principles, we then shall present the state of the art of integrating emotions into the design of educational systems, and notably examine how to create intelligent emotional agents that enhance interaction with users. We will introduce the concept of “socio-emotional climate” as an evaluative indicator of the diversity of desirable interactions within a computer-based learning environment. We formulate the conjecture that a socio-emotional climate capable of enhancing learner motivation, self-assessment and self-motivation could be developed through the use of various socio-emotional agents.
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Emotion is a topic in several computer science subfields. Human-Computer-Interaction (HCI) studies for example the role of affect in human-interface interactions or attempts to design software that express emotions. Artificial intelligence and computational cognitive science may model human thought and behaviour. A new emerging transversal field, affective computing, unites attempts to design emotional software. Interest in emotional computing is grounded in the hypothesis that emotion plays an important role in cognitive processes and therefore has an impact on decision-making and performance (Damasio, 1994; Kort & Reilly, 2002; Picard, 1997).

Educational and learning theories are also concerned with emotion. In constructivist and cognitivist learning theories, learning is a result of cognitive processing and leads to knowledge construction. Learners construct their own reality through interaction with the environment, or at least interpret it based upon their perceptions or experiences. Emotions play a role in all these processes and have the potential to influence learning processes. The learning situation creates a context for a variety of emotional experiences. The effects of emotions on learning are mediated by self regulation and motivation and both positive and negative emotions influence learning. For example, students’ emotions, such as enjoyment, boredom, pride, and anxiety are seen to affect achievement by influencing the student’s involvement and attitude towards learning and learning environments (see e.g., Boekaerts, 2003; Pekrun, 2005).

In addition, working with a computational learning environment puts motivational challenges on the learner and increases the emotional load of the learning situation. For instance, O’Reagan (2003) interviewed 11 students studying online and concludes that the students surveyed positioned emotion as central and essential to the teaching/learning process. So, a learning situation is not only a mental performance, but also an emotional coping situation. According to Wosnitza and Volet (2005), emotions in computer-based learning could be derived from self, context, task or technology and other people. Technical environments should answer students’ needs and expectations and have an influence on their emotional state (Brave & Nass, 2002). The question of how students feel about the environment and technology has been much debated in order to determine the amount of attention they allocate to their learning activities. For example, an impractical environment or unstable technology could distract attention, cause frustration, and disturb the users (Picard & Wexelblat, 2002).

The integration of technologies in education added some effectiveness and efficiency to pedagogical practice. However, little attention has been paid to emotions in educational technology. Some technology-based instructional designs (Astleitner, 2001; MacFadden et al., 2005) not only suggest ways to alleviate problems related to emotional learner states, but also address the more fundamental issue of how to build emotions into the design of learning activities, including collaborative scenarios. In addition, in modern electronic learning environments, emotions also intervene in various person-to-person interaction and person-to-system (human computer) interaction. All these factors contribute to some overall socio-emotional climate.

Key Terms in this Chapter

Socio-Emotional Climate: The socio-emotional climate is evaluative indicator of the full diversity of interactions that seem occur within a computer-based learning environment. The high socio-emotional climate is due to the ability of facilitating social and emotional interactions in order to enhance the learners’ motivation, self-assessment and self-motivation.

Socio-Emotional Agent: A socio-emotional agent is an adaptive computational entity characterized by autonomy, pro-activity, social ability, emotional ability and flexibility. This agent is capable to react, to take initiative, to cooperate, to infer and to learn in an environment that includes a diversity of computer-mediated interactions. The socio-emotional agent can show affective mechanisms and can interact with the users in a socially engaging manner.

Pedagogical Socio-Emotional Agent: A socio-emotional agent is pedagogical when it is particularly developed to facilitate learning. Such an agent should provide emotional support in order to promote a positive mood in the learner, to motivate him/her and to enhance social interaction between learners. This agent should be able to recognise the learner’s emotions and implement a model of the learner’s affect in order to suggest motivational and affective pedagogical strategies.

Emotionally Sound Instruction: Emotionally sound instruction promotes learning and teaching strategies with increased positive and decreased negative emotions. Emotionally sound instruction also may integrate some frustration and imbalance, which is part of the challenge and beneficial for the learning process. It also may contribute to the creation of a sense of community within a learning environment by influencing learner-to-learner interactions.

Emotionally Enhanced Computer-Based Learning Environment: An emotionally enhanced computer-based learning environment supports emotionally and pedagogically sound instruction. Resources and services support a rich set of learning activities and a full diversity of interactions. Such an environment may include interactive learning modules, knowledge bases, communication tools, professional software and cognitive tools. The environment should encourage the growth of learner responsibility, initiative, reflection, decision-making and intentional learning. It also should encourage collaboration and build up confidence with technology.

Emotional Interaction: Emotional interactions are situated intrapersonal, interpersonal, or human-computer interactions necessary for efficient, effective and affective learning. These refer to emotional component processes of appraisal and reappraisal of interactions in order to maintain a close relationship within the learning situation.

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Editorial Advisory Board
Table of Contents
Craig DeLancey
Jordi Vallverdú, David Casacuberta
Chapter 1
Oscar Deniz, Javier Lorenzo, Mario Hernández, Modesto Castrillón
Social intelligence seems to obviously require emotions. People have emotions, recognize them in others and also express them. A wealth of... Sample PDF
Emotional Modeling in an Interactive Robotic Head
Chapter 2
Cyril Laurier, Perfecto Herrera
Creating emotionally sensitive machines will significantly enhance the interaction between humans and machines. In this chapter we focus on enabling... Sample PDF
Automatic Detection of Emotion in Music: Interaction with Emotionally Sensitive Machines
Chapter 3
Christoph Bartneck, Michael J. Lyons
The human face plays a central role in most forms of natural human interaction so we may expect that computational methods for analysis of facial... Sample PDF
Facial Expression Analysis, Modeling and Synthesis: Overcoming the Limitations of Artificial Intelligence with the Art of the Soluble
Chapter 4
Sajal Chandra Banik, Keigo Watanabe, Maki K. Habib, Kiyotaka Izumi
Multi-robot team work is necessary for complex tasks which cannot be performed by a single robot. To get the required performance and reliability... Sample PDF
Multirobot Team Work with Benevolent Characters: The Roles of Emotions
Chapter 5
Matthias Scheutz, Paul Schermerhorn
Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or... Sample PDF
Affective Goal and Task Selection for Social Robots
Chapter 6
Christopher P. Lee-Johnson, Dale A. Carnegie
The hypothesis that artificial emotion-like mechanisms can improve the adaptive performance of robots and intelligent systems has gained... Sample PDF
Robotic Emotions: Navigation with Feeling
Chapter 7
C. Gros
All self-active living beings need to solve the motivational problem—the question of what to do at any moment of their life. For humans and... Sample PDF
Emotions, Diffusive Emotional Control and the Motivational Problem for Autonomous Cognitive Systems
Chapter 8
Bruce J. MacLennan
This chapter addresses the “Hard Problem” of consciousness in the context of robot emotions. The Hard Problem, as defined by Chalmers, refers to the... Sample PDF
Robots React, but Can They Feel?
Chapter 9
Mercedes García-Ordaz, Rocío Carrasco-Carrasco, Francisco José Martínez-López
It is contended here that the emotional elements and features of human reasoning should be taken into account when designing the personality of... Sample PDF
Personality and Emotions in Robotics from the Gender Perspective
Chapter 10
Antoni Gomila, Alberto Amengual
In this chapter we raise some of the moral issues involved in the current development of robotic autonomous agents. Starting from the connection... Sample PDF
Moral Emotions for Autonomous Agents
Chapter 11
Pietro Cipresso, Jean-Marie Dembele, Marco Villamira
In this work, we present an analytical model of hyper-inflated economies and develop a computational model that permits us to consider expectations... Sample PDF
An Emotional Perspective for Agent-Based Computational Economics
Chapter 12
Michel Aubé
The Commitment Theory of Emotions is issued from a careful scrutiny of emotional behavior in humans and animals, as reported in the literature on... Sample PDF
Unfolding Commitments Management: A Systemic View of Emotions
Chapter 13
Sigerist J. Rodríguez, Pilar Herrero, Olinto J. Rodríguez
Today, realism and coherence are highly searched qualities in agent’s behavior; but these qualities cannot be achieved completely without... Sample PDF
A Cognitive Appraisal Based Approach for Emotional Representation
Chapter 14
Clément Raïevsky, François Michaud
Emotion plays several important roles in the cognition of human beings and other life forms, and is therefore a legitimate inspiration for providing... Sample PDF
Emotion Generation Based on a Mismatch Theory of Emotions for Situated Agents
Chapter 15
Artificial Surprise  (pages 267-291)
Luis Macedo, Amilcar Cardoso, Rainer Reisenzein, Emiliano Lorini
This chapter reviews research on computational models of surprise. Part 1 begins with a description of the phenomenon of surprise in humans, reviews... Sample PDF
Artificial Surprise
Chapter 16
Tom Adi
A new theory of emotions is derived from the semantics of the language of emotions. The sound structures of 36 Old Arabic word roots that express... Sample PDF
A Theory of Emotions Based on Natural Language Semantics
Chapter 17
Huma Shah, Kevin Warwick
The Turing Test, originally configured as a game for a human to distinguish between an unseen and unheard man and woman, through a text-based... Sample PDF
Emotion in the Turing Test: A Downward Trend for Machines in Recent Loebner Prizes
Chapter 18
Félix Francisco Ramos Corchado, Héctor Rafael Orozco Aguirre, Luis Alfonso Razo Ruvalcaba
Emotions play an essential role in the cognitive processes of an avatar and are a crucial element for modeling its perception, learning, decision... Sample PDF
Artificial Emotional Intelligence in Virtual Creatures
Chapter 19
Sarantos I. Psycharis
In our study we collected data with respect to cognitive variables (learning outcome), metacognitive indicators (knowledge about cognition and... Sample PDF
Physics and Cognitive-Emotional-Metacognitive Variables: Learning Performance in the Environment of CTAT
Chapter 20
Anthony G. Francis Jr., Manish Mehta, Ashwin Ram
Believable agents designed for long-term interaction with human users need to adapt to them in a way which appears emotionally plausible while... Sample PDF
Emotional Memory and Adaptive Personalities
Chapter 21
Dorel Gorga, Daniel K. Schneider
The purpose of this contribution is to discuss conceptual issues and challenges related to the integration of emotional agents in the design of... Sample PDF
Computer-Based Learning Environments with Emotional Agents
Chapter 22
Emotional Ambient Media  (pages 443-459)
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The “medium is the message”: nowadays the medium as such is non-distinguishable from its presentation environment. However, what is the medium in an... Sample PDF
Emotional Ambient Media
Chapter 23
Jordi Vallverdú, David Casacuberta
During the previous stage of our research we developed a computer simulation (called ‘The Panic Room’ or, more simply, ‘TPR’) dealing with synthetic... Sample PDF
Modelling Hardwired Synthetic Emotions: TPR 2.0
Chapter 24
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Invisibility and Visibility: The Shadows of Artificial Intelligence
About the Contributors