There have been decades of efforts on research and development of intelligent tutoring systems (ITS). ITS assess students' performance from the data collected from the interactions and then adaptively select knowledge objects and pedagogical strategies during the tutoring process to maximize learning effect and minimize learning cost. Delivering content with conversation is always attractive to content authors and students. Research has shown that delivering content through conversation is much more effective than a text. Unfortunately, creating conversational content is difficult. First, in order to have a natural language conversation with a student, the machine has to be able to "understand" the student's natural language input. This involves a research field called "natural language understanding." There isn't a perfect natural language algorithm that can really understand user's free-form speech. Secondly, preparing tutoring speeches for conversations is hard. The essential difficulty is that authors will need to consider the appropriate amount of responses to an infinite possibility of student input. Additionally, it is hard to create and test conversation rules. Conversation rules decide the condition under which a prepared speech is spoken. Since the tutoring conversations often go with other displayed content (e.g., text, image, video) conversation rules need to consider all activity within the learning environment, in addition to the natural language inputs from students. The rule system varies because different environments generate different activity. Creating and testing the rules is also time-consuming. We will try to address these issues and introduce some solutions in this one day tutorial. This tutorial focus on Authoring, Deploying & Data Analysis of Conversational Intelligent Tutoring Systems. We use AutoTutor as the demonstrating ITS in this tutorial. AutoTutor is a research-based system framework funded by the US NSF, IES, DoD, Army and Navy. AutoTutor in this tutorial is a collection of ITS that hold conversations with the human in natural language. AutoTutor has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). All AutoTutor implementations have the following important properties: (1) they use human-inspired tutoring strategies; (2) they use pedagogical agents, and (3) they use technologies that support natural language tutoring. At the end of this Tutorial, we expect participants will be able to (a) create their own conversational ITS using a web-based authoring tool, (b) collect interactive data from their own Conversational ITS and save this data to the standardized database, and (c) extract and analyze the data using Datashop.
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