"I can't do this" and "I'm not good at this" are common statements made by kids while trying to learn. These thoughts, usually triggered by affective states of confusion, frustration, and hopelessness, are some of the greatest problems not being addressed by educational reform. Education has emphasized conveying a great deal of information and facts, and has not modeled the learning process. When teachers present material to the class, it is usually in a polished form that omits the natural steps of making mistakes (feeling confused), recovering from them (overcoming frustration), deconstructing what went wrong (not becoming dispirited), and starting over again (with hope and maybe even enthusiasm). Learning naturally involves failure and a host of associated affective responses. The aim of this project is to build a computerized learning companion that facilitates the child's own efforts at learning. The goal of the companion is to help keep the child's exploration going, by occasionally prompting with questions or feedback, and by watching and responding to the affective state of the child watching especially for signs of frustration and boredom that may precede quitting, for signs of curiosity or interest that tend to indicate active exploration, and for signs of enjoyment and mastery, which might indicate a successful learning experience. The companion is not a tutor that knows all the answers, but a player on the side of the student, there to help him or her learn, and in so doing, learn how to learn better.
—The Program Summary and the Program Description for the Learning Companion. This is the document that was submitted to the National Science Foundation. Or if you do not wish to view this document from a Web page, you can click here to download the summary and description in Word 97 format or in PDF format.
—External Representation of Learning Process and Domain Knowledge: Affective State as a Determinate of its Structure and Function is a paper presented at AI-ED 2001 (Artificial Intelligence in Education) that presents a novel model of learning. This model underpins the evolution of the Learning Companion. Essentially, we believe there is an interplay of emotions and learning, but this interaction is far more complex than previous theories have articulated. This model goes beyond previous research studies not just in the emotions addressed, but also in an attempt to formalize an analytical model that describes the dynamics of emotional states during model-based learning experiences. Click here to download the summary and description in Word 97 format or in PDF format.
—An Affective Model of Interplay Between Emotions and Learning: Reengineering Educational Pedagogy—Building a Learning Companion (PDF format) is a paper presented at ICALT-2001 (International Conference on Advanced Learning Technologies) which won the Best Paper Award. Here are the downloadable PowerPoint Slides for this talk.
—Improving Pedagogy in Developing Nations (PDF format) is a paper submitted to the July 2001 Digital Nations Symposium at the MIT Media Lab.
—Analytical Models of Emotions, Learning and Relationships: Towards an Affect-Sensitive Cognitive Machine (PDF format) is a paper presented at the January 2002 Conference on Virtual Worlds and Simulation (VWSim 2002) held in San Antonio Texas.
—A Pedagogical Model for Teaching Scientific Domain Knowledge (PDF format) is a paper presented at the November 2002 Conference on Frontiers in Education (FIE 2002) held in Boston Massachussetts.
—Theories for Deep Change in Affect-Sensitive Cognitive Machines: A Constructivist Model is an article which appears in the October 2002 special issue of the IEEE Journal of International Forum of Educational Technology & Society (IFETS) and IEEE Learning Technology Task Force.
—We have created a PowerPoint slide show for the Learning Companion. This slide show highlights the concepts of emotions and learning that we hope to embody in the Learning Companion. The slide show can be viewed from this Web site or it can be downloaded as a PowerPoint file, which you can then save to your hard drive.