Using LSA in AutoTutor: Learning Through Mixed-Initiative Dialogue in Natural Language

AutoTutor is a computer tutor that holds conversations with students in natural language (Graesser, Hu, and McNamara, 2005; Graesser, Lu, et al., 2004; Graesser, Person, Harter, and the Tutoring Research Group, 2001; Graesser, VanLehn, Rose, Jordan, and Harter, 2001; Graesser, K. Wiemer-Hastings, P. Wiemer-Hastings, Kreuz, and Harter, 1999). AutoTutor simulates the discourse patterns of human tutors and a number of ideal tutoring strategies. It presents a series of challenging problems (or questions) from a curriculum script and engages in collaborative, mixed initiative dialog while constructing answers. AutoTutor speaks the content of its turns through an animated conversational agent with a speech engine; it was designed to be a good conversational partner that comprehends, speaks, points, and displays emotions, all in a coordinated fashion. For some topics, there are graphical displays, animations of causal mechanisms, or interactive simulation environments (Graesser, Chipman, Haynes, and Olney, 2005). So far, AutoTutor has been developed and tested for topics in Newtonian physics (VanLehn et al., in press) and computer literacy (Graesser, Lu, et al., 2004), showing impressive learning gains compared to pretest measures and suitable control conditions. One notable characteristic of AutoTutor, from the standpoint of the present edited volume, is that latent semantic analysis (LSA) was adopted as its primary representation of world knowledge

A revised algorithm for latent semantic analysis

The intelligent tutoring system AutoTutor uses latent semantic analysis to evaluate student answers to the tutor\u27s questions. By comparing a student\u27s answer to a set of expected answers, the system determines how much information is covered and how to continue the tutorial. Despite the success of LSA in tutoring conversations, the system sometimes has difficulties determining at an early stage whether or not an expectation is covered. A new LSA algorithm significantly improves the precision of AutoTutor\u27s natural language understanding and can be applied to other natural language understanding applications

The right threshold value: What is the right threshold of cosine measure when using latent semantic analysis for evaluating student answers?

AutoTutor is an intelligent tutoring system that holds conversations with learners in natural language. AutoTutor uses Latent Semantic Analysis (LSA) to match student answers to a set of expected answers that would appear in a complete and correct response or which reflect common but incorrect understandings of the material. The correctness of student contributions is decided using a threshold value of the LSA cosine between the student answer and the expectations. In previous work LSA has shown to be effective in detecting good answers of students. The results indicate that the best agreement between LSA matches and the evaluations of subject matter experts can be obtained if the cosine threshold is allowed to be a function of the lengths of both student answer and the expectation being considered. Based on some of our experiences with LSA and AutoTutor, we are developing a new mathematical model to improve the precision of AutoTutor\u27s natural language understanding and discriminative ability. © World Scientific Publishing Company