Keywords: a Vocabulary of Culture and Society

Beauty and brains, pleasure and usability go hand-in-hand in good design.

Emotional Design

We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students' cognitive-affective states during their use of three different computer-based learning environments. Students' cognitive-affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problem-solving game, and problem-solving-based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were compared. We found that boredom was very persistent across learning environments and was associated with poorer learning and problem behaviors, such as gaming the system. Despite prior hypothesis to the contrary, frustration was less persistent, less associated with poorer learning, and did not appear to be an antecedent to gaming the system. Confusion and engaged concentration were the most common states within all three learning environments. Experiences of delight and surprise were rare. These findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the ''vicious cycles'' which occur when a student becomes bored and remains bored for long periods of time.

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Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments

The Origins of Intelligence in Children

AutoTutor simulates a human tutor by holding a conversation with the learner in natural language. The dialogue is augmented by an animated conversational agent and three-dimensional (3-D) interactive simulations in order to enhance the learner's engagement and the depth of the learning. Grounded in constructivist learning theories and tutoring research, AutoTutor achieves learning gains of approximately 0.8 sigma (nearly one letter grade), depending on the learning measure and comparison condition. The computational architecture of the system uses the .NET framework and has simplified deployment for classroom trials.

AutoTutor: an intelligent tutoring system with mixed-initiative dialogue

There is an interplay, between emotions and learning, but this interaction is far more complex than previous theories have articulated. The article proffers a novel model by which to: 1). regard the interplay of emotions upon learning for, 2). the larger practical aim of crafting computer-based models that will recognize a learner's affective state and respond appropriately to it, so that learning will proceed at an optimal pace.

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An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion

This paper presents the first statistically reliable empirical evidence from a controlled study for the effect of human-provided emotional scaffolding on student persistence in an intelligent tutoring system. We describe an experiment that added human-provided emotional scaffolding to an automated Reading Tutor that listens, and discuss the methodology we developed to conduct this experiment. Each student participated in one (experimental) session with emotional scaffolding, and in one (control) session without emotional scaffolding, counterbalanced by order of session. Each session was divided into several portions. After each portion of the session was completed, the Reading Tutor gave the student a choice: continue, or quit. We measured persistence as the number of portions the student completed. Human-provided emotional scaffolding added to the automated Reading Tutor resulted in increased student persistence, compared to the Reading Tutor alone. Increased persistence means increased time on task, which ought lead to improved learning. If these results for reading turn out to hold for other domains too, the implication for intelligent tutoring systems is that they should respond with not just cognitive support but emotional scaffolding as well. Furthermore, the general technique of adding human-supplied capabilities to an existing intelligent tutoring system should prove useful for studying other ITSs too.This paper is a shortened and revised version of Aist et al. (same title). ITS Workshop on Empirical Methods for Tutorial Dialogue. June 4, 2002, San Sebastian, Spain.

/pdf/experimentally-augmenting-an-intelligent-tutoring-system-mx7dd96lzq.pdf

Experimentally augmenting an intelligent tutoring system with human-supplied capabilities: adding human-provided emotional scaffolding to an automated reading tutor that listens

We present a model of the generic learning process and associated metacognitive processes that aid efficient learning. These models can be used as internal representations of a learner’s cognitive-emotive state while engaged in learning. They can also be used to present to the learner a representation of their progress in learning much like a coach o r mentor might use to assist a student. These models and associated representations might be displayed alongside the primary external representation of the subject being learned o r they might be integrated into it. The assessment of the metacognitive processes which shadow the primary learning process needs to be sensitive to the affective state of the learner. As the student proceeds through the learning journey, their affective state may cycle through a wide spectrum of emotions, which influence how efficiently, effectively, and enjoyably they succeed in the learning task. Based upon an understanding and application o f our proposed model, the structure a nd function o f external representations would reflect the learner’s cognitive-emotive state. For example, some representations would provide copious hints that would accelerate the learning at the expense of the playfulness of the e xercise, while other r epresentations might t ransform the learning experience into an enthralling g ame. In p articular these models enable the system designer to provide alternative intervention strategies for the learner who is laboring under a misconception, ranging from a no-nonsense remedial i ntervention to allowing the learner to play out t heir misconceptions in a free-wheeling simulation model that ultimately reveals the folly of their thinking.

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External Representation of Learning Process and Domain Knowledge: Affective State as a Determinate of its Structure and Function

There is an interplay between emotions and learning, but this interaction is far more complex than previous learning theories have articulated. This article proffers a novel model by which to regard the interplay of emotions upon learning and discusses the larger practical aim of crafting computer-based models that will recognize a learner’s affective state and respond appropriately to it so that learning will proceed at an optimal pace.

https://www.jstor.org/stable/jeductechsoci.5.4.56

Theories for Deep Change in Affect-sensitive Cognitive Machines: A Constructivist Model.

There is a growing body of evidence that supports the claim that affect plays a critical role in decision-making and performance as it influences cognitive processes [1], [2], [3]. Despite this body of research the role and function of affect is not generally recognized by the disciplines that address the broad issues of understanding complex systems and complex behavior, especially in the presence of learning. The innovative models and theories that have been proposed to facilitate advancement in the field of human-computer interaction (HCI) tend to focus exclusively on cognitive factors. Consequently, the resulting systems are often unable to adapt to real-world situations in which affective factors play a significant role. We propose several new models for framing a dialogue leading to new insights and innovations that incorporate theories of affect into the design of (affect-sensitive) cognitive machines.

Rob Reilly

Papers

An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion

Experimentally augmenting an intelligent tutoring system with human-supplied capabilities: adding human-provided emotional scaffolding to an automated reading tutor that listens

External Representation of Learning Process and Domain Knowledge: Affective State as a Determinate of its Structure and Function

Theories for Deep Change in Affect-sensitive Cognitive Machines: A Constructivist Model.

An Affective Module for an Intelligent Tutoring System