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This paper describes efforts to both promote and recognize student dialogue in free-entry text discussion within an inquiry-learning environment. First, we discuss collaborative tools that enable students to work together and how these tools can potentially focus student effort on subject matter. We then show how our tutor uses an expert knowledge base to recognize (with 88% success rate) when students are discussing content relevant to the problem and to correctly link (with 70% success) that content with an actual topic. Subsets of the data indicate that even better results are possible. This research provides solid support for the concept of using a knowledge base to recognize content in free-entry text discussion. The paper concludes by demonstrating how this content recognition can be used to support students engaged in problem-solving activities. [ABSTRACT FROM AUTHOR]

This paper describes the use of wireless sensors to recognize student emotion and the use of pedagogical agents to respond to students with these emotions. Minimally invasive sensor technology has reached such a maturity level that students engaged in classroom work can us sensors while using a computer-based tutor. The sensors, located on each of 25 student΄s chair, mouse, monitor, and wrist, provide data about posture, movement, grip tension, facially expressed mental states and arousal. This data has demonstrated that intelligent tutoring systems can provide adaptive feedback based on an individual student΄s affective state. We also describe the evaluation of emotional embodied animated pedagogical agents and their impact on student motivation and achievement. Empirical studies show that students using the agents increased their math value, self-concept and mastery orientation. [ABSTRACT FROM AUTHOR]

We describe technology to dynamically collect information about students΄ emotional state, including human observation and real-time multi-modal sensors. Our goal is to identify physical behaviors that are linked to emotional states, and then identify how these emotional states are linked to student learning. This involves quantitative field observations in the classroom in which researchers record the behavior of students who are using intelligent tutors. We study the specific elements of learner΄s behavior and expression that could be observed by sensors. The long-term goal is to dynamically predict student performance, detect a need for intervention, and determine which interventions are most successful for individual students and the learning context (problem and emotional state). [ABSTRACT FROM AUTHOR]

This demonstration will show how a tutor can detect the content of collaborative behavior and offer relevant domain level interventions. Rashi is a domain independent intelligent tutor providing students with practice using inquiry skills. When working on human biology, students interact with a virtual sick patient whom they must successfully diagnose. Rashi supports students as they create hypotheses and collect data to support and refute these hypotheses. In order to increase the efficacy of Rashi, we incorporated collaborative tools that support group efforts by supporting students as they dynamically share experiences and work together to reach a diagnosis. In addition to this, Rashi contains an intelligent agent that examines collaborative efforts and automatically detects the expert knowledge students are working with. Visitors to this demo will first explore these collaborative tools in detail. Two people will collaborate about a diagnosis and the intelligent agent will examine their collaborative activity and compare it with an expert knowledge base, to determine what domain content is relevant to their activities. The tutor will provide interventions to the visitors that leverage this content recognition. This demonstration provides evidence that expert knowledge bases are a plausible development option for intelligent tutoring systems because they can leverage content recognition to provide more useful feedback. In Rashi, some collaborative content is recognized when students manually match discussion items to expert knowledge. However, the greatest impact comes when the tutor recognizes participants΄ content by matching words and phrases in the chat conversation. Experiments show that the tutor can recognize this content correctly with more than 70% accuracy. Thus, it can provide interventions that suggest what direction students might take if they reached an impasse. This demonstration provides evidence that complicated NLP techniques are not always necessary; a tutor can understand domain level student activity and provide useful interventions using a well-built expert knowledge base. In addition, we show that even though the lack of more complicated techniques may lead to some error in content recognition, we can provide unique forms of feedback that are not detrimental to students when the content is incorrectly recognized, but is significantly helpful when it is correctly recognized. [ABSTRACT FROM AUTHOR]