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Animated pedagogical agents are lifelike animated characters that facilitate the learning process. This paper describes Adele, a pedagogical agent that is designed to work with Web-based educational simulations. The Adele architecture implements key pedagogical functions: presentation, student monitoring and feedback, probing questions, hints, and explanations. These capabilities are coupled with an animated persona that supports continuous multi-modal interaction with a student. The architecture supports client-side execution in a Web browser environment, and is able to inter-operate with simulations created by off-the-shelf authoring tools.

Recent research shows that instructors commonly use politeness strategies to achieve affective scaffolding in educational contexts. The importance of affective factors such as self-confidence and interest that contribute to learner motivation is well recognized. In this paper, we describe the results of a Wizard-of-Oz experiment to study the effect of politeness strategies on both cognitive and motivational factors. We compare the results of two different politeness strategies, direct and polite, in assisting seventeen students in a computer-based learning task. We find that politeness can affect students' motivational state and help students learn difficult concepts. The results of the experiment provide a basis for the design of a polite pedagogical agent and its tutorial intervention strategies.

In this paper we present a novel featureenriched approach that learns to detect the conversation focus of threaded discussions by combining NLP analysis and IR techniques. Using the graph-based algorithm HITS, we integrate different features such as lexical similarity, poster trustworthiness, and speech act analysis of human conversations with featureoriented link generation functions. It is the first quantitative study to analyze human conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Experimental results using a threaded discussion corpus from an undergraduate class show that it achieves significant performance improvements compared with the baseline system.