Physics and Cognitive-Emotional-Metacognitive Variables: Learning Performance in the Environment of CTAT

Physics and Cognitive-Emotional-Metacognitive Variables: Learning Performance in the Environment of CTAT

Sarantos I. Psycharis (University of the Aegean, Greece)
DOI: 10.4018/978-1-60566-354-8.ch019
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Abstract

In our study we collected data with respect to cognitive variables (learning outcome), metacognitive indicators (knowledge about cognition and regulation of cognition) psychological variables (self-esteem) and emotional variables (motives, anxiety). The teaching sequence was implemented using the CTAT authoring tool and the basic teaching unit was referred to fundamental concepts in Mechanics for 20 4th year undergraduate students enrolled in the course «Ápplied Didactics in Natural Sciences» of the University of the Aegean-Department of Education. Analysis of the results shows that anxiety (a negative emotion) can be reduced using CTAT , there is a transfer from extrinsic to intrinsic motivation while metacognitive indicators as well as learning performance can be improved using CTAT . The interactivity of the learning environment influences also self esteem and the results are presented.
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Introduction

Intelligent Tutoring Systems (ITS) have been successful in raising student learning outcome and have been used quite widely in exploring the teaching-learning sequence (TLS). ITS have been shown to be highly effective in improving students’ learning outcome (cognitive performance) in real classrooms (VanLehn2006). The term intelligence corresponds to cognitive skills such as the ability to solve problems, learn, and understand but contemporary research has been focused also on concepts and methods for building programs with emphasis on reasoning rather than on calculating a solution. In ITS problem solving is achieved by applying inference engines to a knowledge base to derive new information, to create new facts and new rules and make decisions. Usually, an inference engine is a software component which reasons over rules when the application is executed while its major task performed is conflict resolution, which determines the sequence of the consultation (Hicks, 2007).

Cognitive/Intelligent Tutors are considered as learning environments based on ITS and cognitive modelling (how people learn) aimed to improve the learning outcome in cognitively-based instructional design .In parallel they deal with cognitive task analysis and exploration of pedagogical content knowledge which is considered as a fundamental ingredient in the cognitively-based instruction (Lovett et al., 2000, Schraagen et al., 2000). Cognitive Tutors learning environments are developed in order to provide a problem-solving environment reinforced with a variety of representation tools, real world scenarios which demand algebraic reasoning, tutorial guidance in the form of step-by-step feedback, specific messages, hints in response to common errors and misconceptions, and -on demand- instructional hints to be used as explanations for the concepts involved in the problem. At ITS systems advised is provided on demand in a progressive way so the learner is involved in the scientific process. ITS has also the property to dynamically adjust to individual learner’s needs and method of approach to the problem. (Aleven et al., 2006). Cognitive Tutors are based primary on the ACT-R(Adaptive Control of Thought-Rational) theory of cognition (Anderson et al., 1995) under the hypothesis that a complex cognitive domain can be understood in terms of small knowledge components called production rules that are learned independently of each other. Production rules represents the target competence that the tutor is meant to help students acquire. Developing a Cognitive Tutor involves creating a cognitive model of student problem solving by writing production rules that characterize the set of strategies and misconceptions. Productions are written in a modular fashion, like in high order programming languages, so that they can apply to a goal and context independent of the specific issue they deal with.

Production rule model (a set of rules about behaviour) is fundamental for the domain intelligence, that is the tutoring system’s knowledge of the specific discipline.

The intelligence of the system uses two algorithms, the model tracing and the knowledge tracing algorithm.

Model tracing algorithm uses the production model in order to interpret each student’s activities and to follow students’ different methodologies and strategic plans as they work through problem solving. The results of model tracing are used to provide students with correctness, error feedback, hints and to provide advice to student about his/her justification and reasoning steps. The main ingredient of the algorithm is the use of comparison techniques, so it can evaluate student’s step against all possible steps provided by the ITS. If the action taken by the student is among these actions, the tutor provides implicit positive feedback, and negative feedback if the model registers incorrect behaviour (correct/incorrect according to the corresponding production rule). The model-tracing algorithm is also used in order to provide hints upon a student’s request. When the student requests a hint, the tutor selects one of the productions that could apply to generate a next step at this point. A hint template attached to this production is filled in with problem specific information and then presented to the student (VanLehn et al., 2005).

Key Terms in this Chapter

Cognitive/Intelligent Tutors: Considered as learning environments based on ITS and cognitive modelling (how people learn) aimed to improve the learning outcome in cognitively-based instructional design

Knowledge about Cognition: The level of the learner’s understanding of his/her own memories, cognitive system, and the way he/she learns.

Self–Esteem: The global perception that we develop in relation to our value as individuals, besides our self-descriptions and our self-evaluations on the various domains of our lives.

Motivation: An internal state that directs and sustains students’ behaviour.

Intelligence: Corresponds to cognitive skills such as the ability to solve problems, learn, and understand but contemporary research has been focused also on concepts and methods for building programs with emphasis on reasoning rather than on calculating a solution.

Production Rules: Represent the target competence that the tutor is meant to help students acquire.

Regulation of Cognition: Refers to how well the learner can regulate his/her own learning system, i.e., goal setting, choosing and applying strategies, and monitoring his/her actions.

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Table of Contents
Foreword
Craig DeLancey
Preface
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
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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
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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
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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
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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
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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
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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
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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?
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Chapter 22
Emotional Ambient Media  (pages 443-459)
Artur Lugmayr, Tillmann Dorsch, Pabo Roman Humanes
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
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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
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Chapter 24
Cecile K.M. Crutzen, Hans-Werner Hein
A vision of future daily life is explored in Ambient Intelligence (AmI). It follows the assumption that information technology should disappear into... Sample PDF
Invisibility and Visibility: The Shadows of Artificial Intelligence
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About the Contributors