Simulating Human Tutor Dialog Moves in AutoTutor

This purpose of this paper is to show how prevalent features of successful human tutoring interactions can be integrated into a pedagogical agent, AutoTutor. AutoTutor is a fully automated computer tutor that responds to learner input by simulating the dialog moves of effective, normal human tutors. AutoTutor’s delivery of dialog moves is organized within a 5step framework that is unique to normal human tutoring interactions. We assessed AutoTutor’s performance as an effective tutor and conversational partner during tutoring sessions with virtual students of varying ability levels. Results from three evaluation cycles indicate the following: (1) AutoTutor is capable of delivering pedagogically effective dialog moves that mimic the dialog move choices of human tutors, and (2) AutoTutor is a reasonably effective conversational partner.  Over the last decade a number of researchers have attempted to uncover the mechanisms of human tutoring that are responsible for student learning gains. Many of the informative findings have been reported in studies that have systematically analyzed the collaborative discourse that occurs between tutors and students (Fox, 1993; Graesser & Person, 1994; Graesser, Person, & Magliano, 1995; Hume, Michael, Rovick, & Evens, 1996; McArthur, Stasz, & Zmuidzinas, 1990; Merrill, Reiser, Ranney, & Trafton, 1992; Moore, 1995; Person & Graesser, 1999; Person, Graesser, Magliano, & Kreuz, 1994; Person, Kreuz, Zwaan, & Graesser, 1995; Putnam, 1987). For example, we have learned that the tutorial session is predominately controlled by the tutor. That is, tutors, not students, typically determine when and what topics will be covered in the session. Further, we know that human tutors rarely employ sophisticated or “ideal” tutoring models that are often incorporated into intelligent tutoring systems. Instead, human tutors are more likely to rely on localized strategies that are embedded within conversational turns. Although many findings such as these have illuminated the tutoring process, they present formidable challenges for designers of intelligent tutoring systems. After all, building a knowledgeable conversational partner is no small feat. However, if designers of future tutoring systems wish to capitalize on the knowledge gained from human tutoring studies, the next generation of tutoring systems will incorporate pedagogical agents that engage in learning dialogs with students. The purpose of this paper is twofold. First, we will describe how prevalent features of successful human tutoring interactions can be incorporated into a pedagogical agent, AutoTutor. Second, we will provide data from several preliminary performance evaluations in which AutoTutor interacts with virtual students of varying ability levels. Person, Graesser, Kreuz, Pomeroy, and the Tutoring Research Group AutoTutor is a fully automated computer tutor that is currently being developed by the Tutoring Research Group (TRG). AutoTutor is a working system that attempts to comprehend students’ natural language contributions and then respond to the student input by simulating the dialogue moves of human tutors. AutoTutor differs from other natural language tutors in several ways. First, AutoTutor does not restrict the natural language input of the student like other systems (e.g., Adele (Shaw, Johnson, & Ganeshan, 1999); the Ymir agents (Cassell & Thórisson, 1999); Cirscim-Tutor (Hume, Michael, Rovick, & Evens, 1996; Zhou et al., 1999); Atlas (Freedman, 1999); and Basic Electricity and Electronics (Moore, 1995; Rose, Di Eugenio, & Moore, 1999)). These systems tend to limit student input to a small subset of judiciously worded speech acts. Second, AutoTutor does not allow the user to substitute natural language contributions with GUI menu options like those in the Atlas and Adele systems. The third difference involves the open-world nature of AutoTutor’s content domain (i.e., computer literacy). The previously mentioned tutoring systems are relatively more closed-world in nature, and therefore, constrain the scope of student contributions. The current version of AutoTutor simulates the tutorial dialog moves of normal, untrained tutors; however, plans for subsequent versions include the integration of more sophisticated ideal tutoring strategies. AutoTutor is currently designed to assist college students learn about topics covered in an introductory computer literacy course. In a typical tutoring session with AutoTutor, students will learn the fundamentals of computer hardware, the operating system, and the Internet. A Brief Sketch of AutoTutor AutoTutor is an animated pedagogical agent that serves as a conversational partner with the student. AutoTutor’s interface is comprised of four features: a two-dimensional, talking head, a text box for typed student input, a text box that displays the problem/question being discussed, and a graphics box that displays pictures and animations that are related to the topic at hand. AutoTutor begins the session by introducing himself and then presents the student with a question or problem that is selected from a curriculum script. The question/problem remains in a text box at the top of the screen until AutoTutor moves on to the next topic. For some questions and problems, there are graphical displays and animations that appear in a specially designated box on the screen. Once AutoTutor has presented the student with a problem or question, a multi-turn tutorial dialog occurs between AutoTutor and the learner. All student contributions are typed into the keyboard and appear in a text box at the bottom of the screen. AutoTutor responds to each student contribution with one or a combination of pedagogically appropriate dialog moves. These dialog moves are conveyed via synthesized speech, appropriate intonation, facial expressions, and gestures and do not appear in text form on the screen. In the future, we hope to have AutoTutor handle speech recognition, so students can speak their contributions. However, current speech recognition packages require time-consuming training that is not optimal for systems that interact with multiple users. The various modules that enable AutoTutor to interact with the learner will be described in subsequent sections of the paper. For now, however, it is important to note that our initial goals for building AutoTutor have been achieved. That is, we have designed a computer tutor that participates in a conversation with the learner while simulating the dialog moves of normal human tutors. WHY SIMULATE NORMAL HUMAN TUTORS? It has been well documented that normal, untrained human tutors are effective. Effect sizes ranging between .5 and 2.3 have been reported in studies where student learning gains were measured (Bloom, 1984; Cohen, Kulik, & Kulik, 1982). For quite a while, these rather large effect sizes were somewhat puzzling. That is, normal tutors typically do not have expert domain knowledge nor do they have knowledge about sophisticated tutoring strategies. In order to gain a better understanding of the primary mechanisms that are responsible for student learning Simulating Human Tutor Dialog Moves in AutoTutor gains, a handful of researchers have systematically analyzed the dialogue that occurs between normal, untrained tutors and students (Graesser & Person, 1994; Graesser et al., 1995; Person & Graesser, 1999; Person et al., 1994; Person et al., 1995). Graesser, Person, and colleagues analyzed over 100 hours of tutoring interactions and identified two prominent features of human tutoring dialogs: (1) a five-step dialog frame that is unique to tutoring interactions, and (2) a set of tutor-initiated dialog moves that serve specific pedagogical functions. We believe these two features are responsible for the positive learning outcomes that occur in typical tutoring settings, and further, these features can be implemented in a tutoring system more easily than the sophisticated methods and strategies that have been advocated by other educational researchers and ITS developers. Five-step Dialog Frame The structure of human tutorial dialogs differs from learning dialogs that often occur in classrooms. Mehan (1979) and others have reported a 3-step pattern that is prevalent in classroom interactions. This pattern is often referred to as IRE, which stands for Initiation (a question or claim articulated by the teacher), Response (an answer or comment provided by the student), and Evaluation (teacher evaluates the student contribution). In tutoring, however, the dialog is managed by a 5-step dialog frame (Graesser & Person, 1994; Graesser et al., 1995). The five steps in this frame are presented below.  Tutor asks question (or presents problem). Step 2: Learner answers question (or begins to solve problem).  Tutor gives short immediate feedback on the quality of the answer (or solution).  Tutor and learner collaboratively improve the quality of the answer.  Tutor assesses learner’s understanding of the answer. This 5-step dialog frame in tutoring is a significant augmentation over the 3-step dialog frame in classrooms. We believe that the advantage of tutoring over classroom settings lies primarily in Step 4. Typically, Step 4 is a lengthy multi-turn dialog in which the tutor and student collaboratively contribute to the explanation that answers the question or solves the problem. At a macro-level, the dialog that occurs between AutoTutor and the learner conforms to Steps 1 through 4 of the 5-step frame. For example, at the beginning of each new topic, AutoTutor presents the learner with a problem or asks the learner a question (Step 1). The learner then attempts to solve the problem or answer the question (Step 2). Next, AutoTutor provides some type of short, evaluative feedback (Step 3). During Step 4, AutoTutor employs a variety of dialog moves (see next section) that encourage learner participation. Thus, ins