1. ILMDA: Intelligent Learning Materials Delivery Agents Goal The ILMDA project is aimed at building an intelligent agent with machine learning capabilities to better deliver learning materials to students. The agent should be able to (1) self-configure its reasoning process to decide which learning materials to deliver and how to deliver them, (2) evaluate the learning materials in terms of their appropriateness, and (3) assess the students’ performance. Faculty Leen-Kiat Soh Suzette Person Students Todd Blank L.D. Miller Ashok Thirunavukkarasu Acknowledgements This work is supported in part by Great Plains Software Technology Initiative, National Center for Information Technology in Education (NCITE), and Computer Science and Engineering (CSE) at the University of Nebraska, Lincoln. Contact Info lksoh@cse.unl.edu sperson@cse.unl.edu (402) 472-6738 (402) 472-1179 http://csce.unl.edu/agents/crush Challenges Corbett et al. (1999): “The arsenal of sophisticated computational modules inherited from AI produce learning gains of approximately.3 to 1.0 standard deviation units compared with students learning the same content in a classroom.” Graesser et al. (2001): “Human tutors produce impressive learning gains (between.4 and 2.3 standard deviation units over classroom teachers), even though the vast majority of tutors in a school’s system have modest domain knowledge, have no training in pedagogical techniques, and rarely use the sophisticated tutoring strategies of ITSs.” Graesser et al. (2001) criticize the current state of tutoring systems: (1) If students merely keep guessing until they find an action that gets positive feedback, they can learn to do the right thing for the wrong reasons – shallow learning; (2) The tutor does not ask students to explain their actions; (3) The user interface of tutoring systems lacks stepping back to see the “basic approach”; and (4) When students learn quantitative skills, they are usually not encouraged to see their work from a qualitative, semantic perspective Corbett, A., J. Anderson, A. Graesser, K. Koedinger, and K. VanLehn (1999). Third Generation Computer Tutors: Learn from or Ignore Human Tutors? in Proceedings of the 1999 Conference of Computer-Human Interaction, 85-86. Graesser, A. C., K. VanLehn, C. P. Rosé, P. W. Jordan, and D. Harter (2001). Intelligent Tutoring Systems with Conversational Dialogue, AI Magazine, 22(4):39-51. Approach Overview ILMDA Reasoning studentComputer & GUI database lectures Historical profile, Real-time behavior Parametric profile of student and environment Retrieval instructions Profile updates Statistics updates Timely delivery of examples & exercise problems Examples Exercise problems Statistics ILMDA Agent Login Successful Login Pick tutorial panel Tutorial panel Methodology Our methodology (1) employs sound artificial intelligence (AI) techniques such as case-based reasoning (CBR), reinforcement learning, dynamic profiling, semantic search, rule-based reasoning, simulated annealing, and so on, and (2) incorporates instructional technology techniques such as adaptive quizzes, learning objects, learner modeling, scaffolding, and so on, and (3) investigates how agents can learn to better deliver learning materials to students. An intelligent agent that interacts with its environment (through its GUI), makes decisions autonomously using CBR, and learns to improve its performance. It has a portable design with a Java-based GUI frontend and a flexible design with a mySQL database backend. New user panel Problem panel Example panel