Why/AutoTutor: A Test of Learning Gains from a Physics Tutor with Natural Language Dialog

Why/AutoTutor is a tutoring system that helps students construct answers to qualitative physics problems by holding a conversation in natural language. Why/AutoTutor provides feedback to the student on what the student types in (positive, neutral, negative feedback), pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects bad answers and misconceptions, answers students’ questions, and summarizes answers. In essence, constructivist learning is implemented in a mixedinitiative dialog. Why/AutoTutor delivers its dialog moves with an animated conversational agent whereas students type in their answers via keyboard. We conducted an experiment that compared Why/AutoTutor with two control conditions (Read textbook, nothing) in assessments of learning gains. The tutoring system performed significantly better than the two control conditions on a test similar to the Force Concept Inventory. AutoTutor and Why/AutoTutor Why/AutoTutor is the most recent tutoring system in the AutoTutor series developed by the Tutoring Research Group at the University of Memphis. Why/AutoTutor was specifically designed to help college students learn Newtonian qualitative physics (Graesser, VanLehn, Rose, Jordan, & Harter, 2001), whereas the previous AutoTutor systems were on topics of introductory computer literacy (Graesser, Person, Harter, & TRG, 2001; Graesser, P. Wiemer-Hastings, K. Wiemer-Hastings, & Kreuz, 1999) and military tactical reasoning (Ryder, Graesser, McNamara, Karnavat, & Pop, 2002). The design of AutoTutor was inspired by three bodies of theoretical, empirical, and applied research. These include explanation-based constructivist theories of learning (Aleven & Koedinger, 2002; Chi, deLeeuw, Chiu, LaVancher, 1994; VanLehn, Jones, & Chi, 1992), intelligent tutoring systems that adaptively respond to student knowledge (Anderson, Corbett, Koedinger, & Pelletier, 1995; VanLehn, Lynch, et al.,2002), and empirical research that has documented the collaborative constructive activities that routinely occur during human tutoring (Chi, Siler, Jeong, Yamauchi, & Hausmann, 2001; Fox, 1993; Graesser, Person, & Magliano, 1995; Moore, 1995; Shah, Evens, Michael, & Rovick, 2002). The process of actively constructing explanations and elaborations of the learning material allegedly produces better learning than merely presenting information to students. This is where human tutors excel in scaffolding learning, because they guide the students in productive constructive processes and simultaneously respond to the student’s information needs. Surprisingly, the dialog moves of most human tutors are not particularly sophisticated from the standpoint of today’s pedagogical theories and those theories implemented in intelligent tutoring systems (Graesser et al., 1995). Human tutors normally coach the student in filling in missing pieces of information in an expected answer and they fix bugs and misconceptions that are manifested by the student during the tutorial dialog. Human tutors rarely implement bona fide Socratic tutoring strategies, modeling-scaffolding-fading, and other intelligent pedagogical techniques (Collins, Brown, & Newman, 1989). The argument has been made that it is the conversational properties of human tutorial dialog, not sophisticated tutoring tactics, that explain why normal human tutors facilitate learning (Graesser et al., 1995). More sophisticated pedagogical techniques will no doubt increase learning even further. AutoTutor was designed to simulate the dialog moves of normal human tutors who coach students in constructing explanations. Why/AutoTutor helps students learn by presenting challenging problems (or questions) from a curriculum script and then engaging in mixed initiative dialog that guides the student in constructing an answer. An example question is “Suppose a boy is in a free-falling elevator and he holds his keys motionless right in front of his face and then lets go. What will happen to the keys? Explain why.” Another example question is “When a car without headrests on the seats is struck from behind, the passengers often suffer neck injuries. Why do passengers get neck injuries in this situation?” Such questions are designed to require about a paragraph of information (3-7 sentences) to answer. However, initial answers to these questions are typically only 1 or 2 sentences in length, even though students have more knowledge that is relevant to an answer. This is where tutorial dialog may be particularly helpful. AutoTutor engages the student in a mixed initiative dialog that assists