VP\u3csup\u3e2\u3c/sup\u3e: The Role of User Modeling in Correcting Errors in Second Language Learning

This paper describes a system, VP2, that has been implemented to tutor non-native speakers in English. The system applies Artificial Intelligence techniques developed in Natural Language research. In particular, it differs from standard approaches by employing a model of its users to customize instruction based on knowledge of the student\u27s native language. The system focuses on the acquisition of English verb-particle and verb-prepositional phrase constructions. It diagnoses errors that students make due to interference of their native language. VP2 recognizes syntactic variation in English sentences, allowing freer translation. VP2 is a modular system: its model of a user\u27s native language can easily be replaced by a model of another language. Its correction strategy is based upon comparison of the native language model with a model of English. The problems and solutions presented in this paper are related to the more general question of how modeling previous knowledge facilitates instruction in a new skill

Towards More Graceful Interaction: A Survey of Question-Answering Programs

One of the aims of Natural Language Processing (NLP) is to facilitate the use of computers by allowing users to interact with systems in natural language. Since such interactions often take the form of question-answering sessions, the process of question-answering is an important part of NLP. In this paper, we are concerned with the progress made towards building question-answering systems which are natural and satisfying to users, allowing them to express themselves freely and answering questions appropriately. Such systems are said to provide graceful interaction. We survey the evolution of question-answering programs, presenting steps and approaches that have been taken towards providing graceful man-machine interaction, and pointing out efficiencies of existing programs and theories. Our emphasis is on the various issues and difficulties encountered in building a user-friendly question-answering program

The role of the crucial experiment in student modelling

As the range of models which tutoring systems can capture is extended, efficient diagnosis becomes more difficult. This thesis describes a solution to this problem based on the generation of 'Critical Problems'; their role in student modelling is analogous to that of the 'Crucial Experiment' in science. We argue that great diagnostic power can be obtained by generating discriminatory problem examples. In general, efficient diagnosis is just not possible without such an hypothesis-testing capability. We describe a program, PO, which given a pair of production rule models and a description of the class of problems which the student must solve, generates an abstract specification of the problems which discriminate between those two hypotheses. Through a process termed 'Abstract Interpretation', PO tips the balance in favour of diagnostic measurement. The key to this problem lies in the realisation that we are only interested in the abstract mapping between a model's inputs and outputs; from the point of view of generating a Critical Problem, the intermediate processing of the model is irrelevant

Towards an evaluation of schema theory with reference to ESL/EFL reading comprehension.

SIGLEAvailable from British Library Document Supply Centre- DSC:D93524 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A study of maladjustment among urban Indian primary school children : a psycho-educational approach.

Thesis (Ph.D.)-University of Durban-Westville, 1978.This is a report of investigations carried out into three aspects of the problem of maladjustment among Indian primary school children. For the sake of convenience and clarity, the dissertation is divided into the following parts, each investigating a different aspect of the topic: (a) A study of the Incidence of Maladjustment among Indian Primary School Children. (b) A Study of the Attitudes of Indian Teachers to Behaviour Problems of Children. (c) An In-depth Comparative Study of Sub-samples of Well-adjusted and Maladjusted Indian Primary School Children in respect of Selected Aspects of their Home Environment. The research was carried out in the form of three projects corresponding to the title order given above and referred to in this report as Projects One, Two, and Three, respectively. All three projects are linked together by the common theme of "maladjustment"

The characterization of communal knowledge : case studies in knowledge relevant to science and schooling

This work attempts the task of analysing communal knowledge in relation to schooled knowledge. At one level. the thesis concerns a peculiar method of measuring land (cubarfio) used by peasants in Brazil and their understanding of the transformations of soil. At another level. it attempts to look outwards all the time to some very general issues so as to discuss Questions about the relative \'ah,lation of school knowledge and communal knowledge; the distance between educational discourse on the one hand and the teachers and ordinary people's discourse on the other: together with a discussion of knowledge elicitation, representation and acquisition.\ud The account of the specific communal knowledge described in the thesis is based on a empirical study with adults in a rural community in Brazil and data is quaiirativc. Information is obtained mainly from farm-workers and indigenous primary school teachers. Teachback, in the sense proposed by Pask, is the central precess around which 'conversations' between participants take place.\ud Research in Science Education has very largely treated knowledge from an essentially individual Doint of view. In this thesis. however, knowledge is regarded as a social entity realized in individual discursive action. Knowing becomes being a particiDant in a discourse and to possess knowledge is turned into to be able to operate a certain kind of discursive process.\ud The goal of trying to reach understanding leads the informants to create new explanations, and to think explicitly about the taken-for-granted discourse. This gives the researcher, the possibility of a further level of analysis about the discourse (not just of structures R'ithin the discourse). As an outcome. novel results concerning methods of land measurement serve as an example to place the knowledge of cubacao in relation to historical knowledge structures and the mechanisms of social transmission and reproduction of knowledge. \u

The preparation of English language teachers in Malaysia : a video-based approach.

SIGLEAvailable from British Library Document Supply Centre- DSC:D178020 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Electronic homework: an intelligent tutoring system in mathematics.

by Lee Fong-lok.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 309-323).Questionnaires and some appendixes in Chinese.TABLE OF CONTENTS --- p.iiTABLES --- p.viiFIGURES --- p.viiiACKNOWLEDGMENTS --- p.ixABSTRACT --- p.xiChapter 1 --- INTRODUCTION --- p.1HOW COMPUTERS CAN HELP OUR CHILDREN --- p.2How Human Tutors Tutor --- p.7"Can Computers "" Think""?" --- p.11Intelligent Tutoring Systems --- p.17ELECTRONIC HOMEWORK --- p.18A Personal Tutor to Students --- p.18The Present Study 226}0ؤ An Investigation into Electronic Homework --- p.23How to Build up Electronic Homework --- p.25Effect of using Electronic Homework --- p.29The Future of Electronic Homework --- p.29CHAPTER SUMMARY --- p.30Chapter 2 --- REPRESENTATION OF KNOWLEDGE --- p.32OVERVIEW --- p.32HOW KNOWLEDGE IS REPRESENTED --- p.33SYMBOLIC EXPRESSIONS OR NEURAL NETWORKS --- p.36PROCEDURAL AND DECLARATIVE KNOWLEDGE --- p.37On Evidence Supporting the Procedural- Declarative Knowledge Distinction --- p.39Distinction of Knowledge --- p.49EXPLICIT VERSUS IMPLICIT KNOWLEDGE --- p.52DEGREE OF SOPHISTICATION VERSUS PROCEDURALIZATION --- p.53NOTATION OF KNOWLEDGE --- p.59What Should Be Done But Not What Is Actually Done --- p.62CHAPTER SUMMARY --- p.63Chapter 3 --- WHAT KNOWLEDGE TO INCORPORATE AND HOW --- p.67OVERVIEW --- p.67SEPARATE STORAGE FOR DIFFERENT TYPES OF KNOWLEDGE --- p.69DIFFERENT TYPES OF KNOWLEDGE --- p.70The Expert module --- p.71The Student Module --- p.78The Tutoring Module --- p.85The Communication Module --- p.121CHAPTER SUMMARY --- p.124Chapter 4 --- PROBLEM COMPLEXITY AND INDIVIDUAL DIFFERENCES --- p.127OVERVIEW --- p.127COGNITIVE DIFFICULTY OR SIMPLE ITEM DIFFICULTY RATIO --- p.129DIFFICULTY LEVEL OBTAINED BEFORE TEST ADMINISTRATION --- p.130OTHER MEASURES OF PROBLEM DIFFICULTY --- p.131Complexity of Problems --- p.132Problem Complexity Level --- p.133INDIVIDUAL DIFFERENCES --- p.133Chapter 5 --- HOW TO IMPLEMENT AND EVALUATE THE SYSTEM…… --- p.136OVERVIEW --- p.136KNOWLEDGE ACQUISITION --- p.140Expert Module --- p.141Student Module --- p.142Tutoring Module --- p.149Problem Difficulty --- p.155IMPLEMENTATION --- p.161Implementation of Knowledge into Computer Tutor --- p.161EVALUATION --- p.162Formative Evaluation --- p.162Summative Evaluation --- p.163CHAPTER SUMMARY --- p.167Chapter 6 --- KNOWLEDGE ACQUIRED --- p.169OVERVIEW --- p.169EXPERT MODULE --- p.170STUDENT MODULE --- p.172Mal-rules --- p.172Strategies for Handling Mal-rules --- p.176Understanding the Errors --- p.177Section Summary --- p.209TUTORING MODULE --- p.210Effects of tutoring --- p.210Scores in Posttest and Ceiling Effect --- p.214Effects of Practice and Tutoring Methods on Retention test --- p.214How Experienced Teachers Perceive --- p.221CHAPTER SUMMARY --- p.228Chapter 7 --- PROBLEM DIFFICULTY --- p.230OVERVIEW --- p.230RESULTS OF DIFFERENT MEASURES OF PROBLEM DIFFICULTY --- p.231Students' estimation of Item Difficulty --- p.232Item Difficulty Ratio --- p.234Teachers' Estimation of Problem Difficulty --- p.234Predicted Complexity --- p.237CORRELATION AMONG THE VARIOUS MEASURES OF PROBLEM DIFFICULTY --- p.243How students rate the problems --- p.245PREDICTING THE PROBLEM DIFFICULTY MEASURES --- p.246About the Three Measures --- p.249Practical Considerations --- p.252PROBLEM COMPLEXITY --- p.254USING PROBLEM COMPLEXITY IN ELECTRONIC HOMEWORK --- p.258CHAPTER SUMMARY --- p.258Chapter 8 --- SYSTEM EVALUATION --- p.259OVERVIEW --- p.259THE EVALUATION --- p.260Formative Evaluation --- p.260Summative Evaluation --- p.270DISCUSSION --- p.288Who Benefit From Using The System --- p.288Hardware Constraints --- p.289Human-computer interface --- p.289Effect on the use of Electronic Homework --- p.290Expert-Novice Differences --- p.292CHAPTER SUMMARY --- p.293Chapter 9 --- CONCLUSIONS AND DISCUSSION --- p.294OVERVIEW --- p.294THEORETICAL ASPECTS --- p.295Why and how do students make errors? --- p.296What makes an expert tutor? --- p.302KNOWLEDGE OBTAINED --- p.304CAN ELECTRONIC HOMEWORK HELP STUDENTS AND TEACHERS? --- p.305Purposes of the Evaluation --- p.305Results of The Evaluation --- p.306SUGGESTIONS --- p.306Machine Learning --- p.307Input Systems --- p.307Better understanding of Human Problem Solving Process --- p.307REFERENCES --- p.309Appendix A: Mal-rule Collecting Tests ……… --- p.324Appendix B: Test on Solving Algebraic Equations --- p.334Appendix C: Tutoring Scripts --- p.336Appendix D: Manipulative Rules Used In Solving Algebraic Equations --- p.338Appendix E: Remediation Rules Used In Solving Algebraic Equations --- p.339Appendix F: List of Mal-rules --- p.341Appendix G: Teachers' Estimation of Problem Difficulty --- p.344Appendix H: Learning Process Questionnaire --- p.349Appendix I: Questionnaire on the Use of Electronic Homework --- p.344Appendix J: Teachers' Perception on Electronic Homework --- p.347Appendix K: Students' Perception on the Use of Electronic Homework in Formative Evaluation --- p.346Appendix L: Results of Students' Perception on Electronic Homework --- p.347Appendix M: Students' Scores in Learning Process Questionnaire --- p.349Appendix N: Homework 1 --- p.355Appendix O: Homework 2 --- p.358Appendix P: Students' Retention Test Scores --- p.361Appendix Q: Results of Teachers' Perception on Electronic Homework --- p.366Appendix R: Transcript of Students' Interview --- p.368Appendix S: Installation and Source Code --- p.40