redictingmechanicalreasoningability(reasoningwiththenavy)預(yù)測機械推理能力與海軍的推理(已修改)
2025-01-24 08:40 本頁面
【正文】 AutoTutor: An Intelligent Tutoring System with Mixed Initiative Dialog Art Graesser University of Memphis Department of Psychology amp。 the Institute for Intelligent Systems Supported on grants from the NSF, ONR, ARI, IDA, IES, US Census Bureau, and CHI Systems Interdisciplinary Approach Computer Science Psychology Computational Linguistics Education Overview ? Brief ments on my research on question asking and answering ? Primary focus is on AutoTutor a collaborative reasoning and question answering system Overview of my Research on Questions ? Psychological Models ? Question asking (PREG, ONR, NSF, ARI) ? Question answering (QUEST, ONR) ? Computer Artifacts ? Tutor (AutoTutor, Why/AutoTutor, Think like a mander, NSF, ONR, ARI, CHI Systems) ? Survey question critiquer (QUAID, US Census, NSF) ? Point amp。 Query software (Pamp。Q, ONR) ? Querybased information retrieval (HURA Advisor, IDA) AutoTutor Collaborative reasoning and question answering in tutorial dialog Think Like a Commander Vigtes 1 Trouble in McLouth 2 Save the Shrine 3 The Recon Fight 4 A Shift In Forces 5 The Attack Begins 6 The Bigger Picture 7 Looking Deep 8 Before the Attack 9 Meanwhile Back at the Ranch ?Keep Focus on Mission? Higher’s Intent? ?Model a Thinking Enemy? ?Consider Effects of Terrain? ?Use All Assets Available? ?Consider Timing? ?See the Bigger Picture? ?Visualize the Battlefield Accurately? Realistic SpaceTime Forecast Dynamically? Entities Change Over Time Proactively? What Can I Make Enemy Do ?Consider Contingencies and Remain Flexible? What does AutoTutor do? ? Asks questions and presents problems Why? How? Whatif? What is the difference? ? Evaluates meaning and correctness of the learner’s answers (LSA and putational linguistics) ? Gives feedback on answers ? Face displays emotions + some gestures ? Hints ? Prompts for specific information ? Adds information that is missed ? Corrects some bugs and misconceptions ? Answers student question ? Holds mixedinitiative dialog in natural language Pedagogical Design Goals ? Simulate normal human tutors and ideal tutors ? Active construction of student knowledge rather than information delivery system ? Collaborative answering of deep reasoning questions ? Approximate evaluation of student knowledge rather than detailed student modeling ? A discourse prosthesis Feasibility of Natural Language Dialog in Tutoring ? Learners are fiving when the tutor’s dialog acts are imperfect. ? They are even more fiving when the bar is set low during instructions. ? There are learning gains. ? Learning is not correlated with liking. Low Expected Precision High Expected Precision Low Common Ground YES MAYBE High Common Ground MAYBE NO DEMO Human Tutors ? Analyze hundreds of hours human tutors ? Research methods in college students ? Basic algebra in 7th grade ? Typical unskilled crossage tutors ? Studies from the Memphis labs ? Graesser amp。 Person studies ? Studies from other labs ? Chi, Evens, McArthur … Characteristics of students that we wish were better ? Student question asking ? Comprehension calibration ? Selfregulated learning, monitoring, amp。 and error correction ? Precise, symbolic articulation of knowledge ? Global integration of knowledge ? Distant anaphoric reference ? Analogical reasoning ? Application of principles to a practical problem Pedagogical strategies not used by unskilled tutors ? Socratic method (Collins, Stevens) ? Modelingscaffoldingfading (Rogoff) ? R
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