Intelligent Computer Assisted Language Learning Research Articles

Mr. Collins: A collaboratively constructed, inspectable student model for intelligent computer assisted language learning

This paper describes ‘Mr. Collins’, a student model for intelligent computer assisted language learning (ICALL), which is based on the results of empirical studies conducted in the target domain. ‘Mr.Collins’ is constructed and updated through student/system collaboration. This collaborative approach to learner modelling aims both to promote student reflection through discussion of the student-model, and to provide more detailed and accurate information for the model. Due to his own contributions the student is less likely to regard the model with suspicion. Because the student model is the focus of the system, it must contain more information than is typically found in learner models. Therefore, in addition to domain knowledge and misconceptions, ‘Mr. Collins’ also models learner confidence, background languages and learning strategies. The typical acquisition sequence for the target rules is explicitly modelled to provide an additional perspective from which the system may assess learner input.

Extending the scope of the student model

In this paper we maintain that there are benefits to extending the scope of student models to include additional information as part of the explicit student model. We illustrate our argument by describing a student model which focuses on 1. performance in the domain; 2. acquisition order of the target knowledge; 3. analogy; 4. learning strategies; 5. awareness and reflection. The first four of these issues are explicitly represented in the student model. Awareness and reflection should occur as the student model is transparent; it is used to promote learner reflection by encouraging the learner to view, and even negotiate changes to the model. Although the architecture is transferable across domains, each instantiation of the student model will necessarily be domain specific due to the importance of factors such as the relevant background knowledge for analogy, and typical progress through the target material. As an example of this approach we describe the student model of an intelligent computer assisted language learning system which was based on research findings on the above five topics in the field of second language acquisition. Throughout we address the issue of the generality of this model, with particular reference to the possibility of a similar architecture reflecting comparable issues in the domain of learning about electrical circuits.

Student modelling for second language acquisition

This paper describes the student model of an intelligent computer assisted language learning (ICALL) system which is based on current theories in the field of second language acquisition (SLA). The following four issues were selected as important questions in SLA, while also being implementable in the ICALL environment: (1) the acquisition order of the target rules; (2) language learning strategies; (3) language transfer; and (4) language awareness. The first three points are represented in the student model. A learner's language awareness is enhanced by explicit user-system discussion of these issues and the target constructions, and also by cooperative construction and repair of the student model. The project aims both to test the suitability of these theories in the context of ICALL, and at the same time develop a system which demonstrates guidelines for the creation of more successful and acceptable ICALL systems. The area of language used for illustration is clitic pronoun placement in European Portuguese.

Learning languages: Implications for student modelling in ICALL

Many factors affect the learning of a foreign language. When designing computer assisted language learning software it is usually not sufficient to think only about creating an exercise in the language, but students should be modelled in order to allow the program to take account of individuals' beliefs and learning. However, student models are criticised for various reasons, the most common of which include: 1. Modelling the learner places a great burden on the system, as it has sole responsibility for the creation of an accurate student model. 2. Student models are inadequate because it is not possible to model all aspects of a student's knowledge and learning. This paper describes the student model of an intelligent computer assisted language learning (ICALL) system which strives to overcome problems of traditional student models by taking into account issues important in the field of second language acquisition, and research in collaborative approaches to learning.

Towards user/system collaboration in developing a student model for intelligent computer assisted language learning

This paper examines research in the field of collaborative learning, concentrating on aspects suitable for use in an intelligent computer assisted language learning (ICALL) system. Current work on the project focuses on the construction of a student model, based on the notion that a model constructed and repaired through collaboration between the system and student will lead to a more accurate representation of the learner's beliefs, and is less likely to be regarded with suspicion by the student. Further, the act of collaboration should promote reflection on both the language to be learnt and the student's approaches to learning.

Is intelligent computer-assisted language learning possible?

The limitations of existing CALL software [Self, J. (1985) Microcomputers in Education. Harvester Press] and the extravagant claims of achievements in artificial intelligence [Schank, R. (1984) The Cognitive Computer: On Language, Learning and Artificial Intelligence. Addison-Wesley] together with rapidly increasing power/price ratio of microcomputers has led to much speculation about the development of intelligent computer-assisted learning. Intelligent computer-assisted learning systems consist of four modules: a natural language interface, a pedagogical module, a model of the student, and a knowledge base of the subject domain [Dede, C. (1986) Int. J. Man-Machine Studies 24, 329–353]. The three latter modules are still at very early experimental stages of development and pose formidable problems. The basis of current research in artificial intelligence is the physical symbol system hypothesis [Newell, A. and Simon, H. (1976) Communications of the ACM 19, 113–126. It is argued that no knowledge base of the subject domain language can be constructed on that basis [Winograd, T. (1987) Paper presented at the Humans, Animals, and Machines: Boundaries and Protections Conference, Stanford University]. Thus, inherent limitations of the physical symbol system hypothesis make (current) artificial intelligence techniques inappropriate for the development of intelligent CALL. However, teachers can use computers in language teaching in ways which are adapted to pedagogical goals. In this sense at least, intelligent computer-assisted language learning is possible.

Using intelligent computer-assisted language learning

This paper explores uses for intelligent courseware in language classrooms. Definitions are given for three types of intelligent systems: microworlds, intelligent grammar checkers, and intelligent tutoring systems. Uses of these systems are explained for the prewriting, drafting, and revising phases of writing instruction and for second language instruction focusing on formal and functional aspects of the target language. Questions for future research on intelligent language courseware are raised.

Computer assisted learning: from process control paradigm to information resource paradigm

Computer assisted learning (CAL) has several dimensions. This article highlights only one of those dimensions: the degree to which the computer controls the learning process during a CAL session. Until recently the dominant guiding principle for CAL was the tutor metaphor. Experience with three decades of CAL in operation (only occasionally with satisfactory results), and two decades of research on intelligent CAL has brought increasing doubts about the general value of this guiding principle. The basic problem with ICAL is that we still don't know much about the fundamental principles of tutoring. We believe that research on the subject of intelligent tutoring is important for psychology of learning and instruction but also that it should be treated as such. Intelligent tutoring cannot be presented as THE solution for the shortcomings of traditional CAL. I will argue that dissatisfaction in general with traditional CAL, recognition of fundamental problems with ICAL for most domains and the advent of low cost high density storage media make the time right to direct more attention to multimedial information resources.

Some Psychological Aspects of Computer‐Assisted Learning and Teaching

Abstract Three aspects of computer‐assisted learning (CAL) are considered, namely the design of instructional programs, the software tools needed for their realization, and the assimilation of CAL within the classroom. Under this framework, developments in CAL are discussed by noting the expansion from task‐based programs, influenced by Skinnerian principles of learning, to knowledge‐based systems deriving from a cognitive view of psychology. This latter approach emphasizes learner initiatives for developing and organizing their own knowledge structures. Finally, the techniques of knowledge representation arising from artificial intelligence are outlined, and their value for student modelling and for designing more intelligent CAL programs, are discussed. The recommendation is for a greater involvement of psychologists in this work.

New Trends in Computer Assisted Learning (CAL)

Teaching and learning by programmed instruction is independent of the tool computer. But as soon as computers were able to present strategies of programmed learning, computer scientists and educators startet to invent and implement completely new strategies of computer assisted learning and problem solving. They made more extensive use of the computer's facilties than CAI (computer aided instruction) did. This paper overviews some modern trends of computer assisted learning (CAL) by presenting examples like “intelligent CAL” training of blind people by CALstrategies, and presenting CAL-programs by public communication networks. CAL is considered as a discipline of man-machine-communication which contributed from a very early stage of MMC to improvements of man-computer interfaces.