Using Latent Semantic Analysis to Assess Reader Strategies

We tested a computer-based procedure for assessing reader strategies that was based on verbal protocols that utilized latent semantic analysis (LSA). Students were given self-explanation-reading training (SERT), which teaches strategies that facilitate self-explanation during reading, such as elaboration based on world knowledge and bridging between text sentences. During a computerized version of SERT practice, students read texts and typed self-explanations into a computer after each sentence. The use of SERT strategies during this practice was assessed by determining the extent to which students used the information in the current sentence versus the prior text or world knowledge in their self-explanations. This assessment was made on the basis of human judgments and LSA. Both human judgments and LSA were remarkably similar and indicated that students who were not complying with SERT tended to paraphrase the text sentences, whereas students who were compliant with SERT tended to explain the sentences in terms of what they knew about the world and of information provided in the prior text context. The similarity between human judgments and LSA indicates that LSA will be useful in accounting for reading strategies in a Web-based version of SERT

Improving an intelligent tutor's comprehension of students with Latent Semantic Analysis

AutoTutor is an intelligent tutor that interacts smoothly with the student using natural language dialogue. This type of interaction allows us to extend the domains of tutoring. We are no longer restricted to areas like mathematics and science where interaction with the student can be limited to typing in numbers or selecting possibilities with a button. Others have tried to implement tutors that interact via natural language in the past, but because of the di#culty of understanding language in a wide domain, their best results came when they limited student answers to single words. Our research directly addresses the problem of understanding what the student naturally says. One solution to this problem that has recently emerged is Latent Semantic Analysis (LSA). LSA is a statistical, corpus-based natural language understanding technique that supports similarity comparisons between texts. The success of this technique has been described elsewhere [3, 5, for example]. In thi..

Approximate Natural Language Understanding for an Intelligent Tutor

Intelligent tutoring systems (ITS's) have a rich history of helping students in certain scientific domains, like geometry, chemistry, and programming. These domains are ideal for ITS's, because they can be easily represented and because the type of interaction between the student and the tutor can be limited to entering a few simple numbers, symbols, or keywords. Students need help in other areas, but without the ability to robustly understand a student's input, ITS's in these areas are inherently limited. Recently a technique called Latent Semantic Analysis has offered a corpus-based approach to understanding textual input which is not sensitive to errors in spelling or grammar -- in fact, it pays no attention to word order at all. We are using this technique as part of an ITS system which promotes learning using natural human-like dialogue between the human and the student. This paper describes the tutoring system and Latent Semantic Analysis, and how they operate together. Then it de..

AutoTutor: A simulation of a human tutor

AutoTutor is a computer tutor that simulates the discourse patterns and pedagogical strategies of a typical human tutor. AutoTutor is designed to assist college students in learning the fundamentals of hardware, operating systems, and the Internet in an introductory computer literacy course. Most tutors in school systems are not highly trained in tutoring techniques and have only a modest expertise on the tutoring topic, but they are surprisingly effective in producing learning gains in students. We have dissected the discourse and pedagogical strategies these unskilled tutors exhibit by analyzing approximately 100 hours of naturalistic tutoring sessions. These mechanisms are implemented in AutoTutor. AutoTutor presents questions and problems from a curriculum script, attempts to comprehend learner contributions that are entered by keyboard, formulates dialog moves that are sensitive to the learner\u27s contributions (such as short feedback, pumps, prompts, elaborations, corrections, and hints), and delivers the dialog moves with a talking head. AutoTutor has seven modules: a curriculum script, language extraction, speech act classification, latent semantic analysis, topic selection, dialog move generation, and a talking head. © 1999 Elsevier Science B.V. All rights reserved