Computer-assisted Instruction System Research Articles

Intelligent Tutor, Environment and Manual for Introductory Programming

Programming is perhaps the most computerized of all academic disciplines. Special computer systems help teachers and students in all kinds of programming activity. Computer-assisted instruction (CAI) systems -`coachers' and `tutors' help teachers in explaining new material, presenting examples, and giving and checking various tests. Students can independently design and debug programs with the help of studentoriented programming environments. Efficient access to the previously learned information can be accomplished with the help of computer manuals and online help systems.

Student modeling to support multiple instructional approaches

Intelligent computer-assisted instruction (ICAI) systems have continually sought increased flexibility to respond appropriately to the multi-faceted interests of students. Research on theImage student modeler of theGuidon2 ICAI system has developed amultiple-anticipation approach to plan generation and interpretation that directly meets a wide range of communication goals: providing information support, encouraging exploration with interesting elaborations, recognizing strategic mistakes in actions and plans, evaluating success in domain tasks, diagnosing misconceptions, and recommending improvements for mistakes.

An Intelligent Computer-Assisted Instruction System Designed for Rural Health Workers in Developing Countries

This paper describes an intelligent computer-assisted instruction system that was designed for rural health workers in developing countries. This system, called Consult-EAO, includes an expert module and a coaching module. The expert module, which is derived from the knowledge-based decision support system Tropicaid, covers most of medical practice in developing countries. It allows for the creation of outpatient simulations without the help of a teacher. The student may practice his knowledge by solving problems with these simulations. The system gives some initial facts and controls the simulation during the session by guiding the student toward the most efficient decisions. All student answers are analyzed and, if necessary, criticized. The messages are adapted to the situation due to the pedagogical rules of the coaching module. This system runs on PC-compatible computer.

Third World continuing medical education with hypertext: the Liverpool Anaemia Guide System

Abstract Some of the needs of continuing medical education (CME) in Third World countries are described. The potential of hypertext-based computer-assisted instruction (CAI) to meet these needs are discussed and compared to a more traditional expert system type approach to CAI. A prototype hypertext CAI system in such an application area—the Liverpool Anaemia Guide System (LAGS)—is described and its use evaluated by a small sample of Third World medical practitioners. It is concluded that systems such as LAGS may make a valuable and cost effective contribution to Third World CME programmes.

STABILITY AND CHANGE IN AFFECTIVE CHARACTERISTICS OF TEACHERS: CAN COMPUTER ENVIRONMENT MAKE A DIFFERENCE?

Summary. The purpose of the present study was to investigate the effects of Computer Assisted Instruction (CAI) on teaching self‐concept (TSC) and teachers' perceptions regarding allocated time to various instructional activities. Two groups of elementary school teachers (N=218) participated in the study: about half of the teachers (N=113) adopted a CAI system for drill and practice, while the others did not use computers in the classroom. All teachers were assessed prior to the installation of the CAI system in the schools and a year later. Results showed positive changes in affective characteristics of CAI teachers who were involved in school level decision‐making, while themain effects of CAI were not significant. The theoretical and practical implications of the findings are discussed.

Specification and Design Procedures, Functions, and Issues in Developing Intelligent Tutoring Systems

Intelligent tutoring systems comprise an important new technology within the field of artificial intelligence. This article explores specification and design procedures, functions, and issues in developing intelligent tutoring systems-those features which are necessary to consider when designing or developing intelligent computer-assisted instruction (ICAI) systems. Instead of developing a specific ITS in a particular domain, this article explores generic procedures for developing intelligent tutoring systems. The unstructured proliferation of intelligent instruction systems with a variety of functionality makes this effort particularly important.

Computer Assisted Instruction System with Laser Disc for Neurological Training

Computer Assisted Instruction System with Laser Disc for Neurological Training

Cost-effective development of a computer-assisted instruction system.

Cost-effective development of a computer-assisted instruction systemRB McGhee, Jr, WF Bennett, CS Morris and LS WitanowskiAudio Available | Share

A multidisciplinary model for development of intelligent computer-assisted instruction

Development of an intelligent computer-assisted instructional (ICAI) system requires a multidisciplinary team effort. A schematic model is proposed to help ICAI developers identify the types of required expertise and integrate them into a system. Three types of expertise are discussed in the model: (a) domain expertise, (b) domain engineering expertise, (c) and instructional expertise. Domain expertise is classified into two types: conceptual knowledge and performance (procedural) expertise. Specific knowledge representation methods are proposed to represent the two types of domain—semantic network for conceptual knowledge and production rules for procedural knowledge—and frames-scripts for the combined domain of the two types. For domain engineering expertise, three technical methods are discussed: knowledge acquisition, task analysis, and knowledge representation. For instructional expertise, conceptual issues and technical methods to model student learning and to select instructional strategies are discussed. A multidisciplinary cooperative effort is reemphasized in the conclusion.

Comparison of student performance in arithmetic exercises: TOAM vs paper-and-pencil testing

The increasing usage of Computer-Assisted Instruction systems in schools raises several methodological questions. Among them: Are the evaluation results obtained within the computer system comparable with those obtained with classical paper-and-pencil testing? Is the computer usage favoring certain pupil populations? How much emphasis must be placed in teaching not only the curriculum contents but also the media usage? An experiment was conducted to analyse the difference in performance of elementary school pupils when working either with paper-and-pencil or with a computer terminal. This paper presents the conception of the experiment, its design, the results obtained, and their analysis.

Artificial intelligence knowledge acquisition techniques for instructional development

Knowledge engineering techniques for developing expert systems may also be useful for instructional development. A review of knowledge engineering focusing on knowledge representation and knowledge acquisition suggests ways in which these methods could be adapted to developing instructional systems. As further work is done on intelligent computer-assisted instructional systems and other complex instructional development projects, knowledge engineering skills may become more important for the instructional developer.

Socioeconomic Status, Aptitude, and Gender Differences in CAI Gains of Arithmetic

AbstractThis study examined the effects of computer-assisted instruction (CAI) in relation to students’ socioeconomic status (SES), aptitude, and gender. The population consisted of 99 disadvantaged and 112 advantaged students of three cohorts in two schools. All students used the same CAI system for drill-and-practice programs in arithmetic. I collected reports of CAI performance from 3rd through 6th graders at the beginning and end of each school year. Results of a repeated-measure design analysis showed performance at higher grade-equivalent CAI levels and larger gains from the CAI work, which were statistically significant, for advantaged over disadvantaged students, for high achievers over low achievers, and for boys over girls. Interactions of SES and aptitude indicated that those who benefited most from the CAI work were the high achievers in the advantaged population. “The belief that computers can provide benefits and the optimistic expectation that they are supplying these benefits has fueled a ...

An analysis of strategies used to disseminate the PLATO system

Two aspects of efforts to disseminate an innovative computer-assisted instructional system called PLATO between 1972 and 1976 are addressed. The first pertains to the dissemination problems encountered and resolved by the system developers. The second covers dissemination modus operandi chosen by the developers to bring PLATO to the attention of large numbers of people. An ex post facto analysis of data obtained suggested PLATO developers made dissemination decisions that reflected viable dissemination theory and practice of the time, committed substantial resources to the dissemination process, and selected what appeared to be a cost-effective marketing strategy. Perhaps the most important consequence of these actions is that PLATO flourished throughout the 1970s and continues to flourish.

Computer-Based Drill and Practice in Arithmetic: Widening the Gap Between High- and Low-Achieving Students

Precollege schools use their computers predominantly for drill and practice in arithmetic. It has been shown by research to be the most effective application of CAI (Computer-Assisted Instruction). The present study used naturalistic methods to examine differential effectiveness of CAI in arithmetic for high- and low-achieving students. Seven elementary school students with varied backgrounds with regard to socioeconomic status (SES), grade level, type of school, and achievements in arithmetic participated in the study. The study identifies strategies that students use to advance effectively through the levels of the CAI practice with material not yet studied in class. The study also discusses software- and hardware-related errors that students make while working with the CAI system. In many cases, these errors are unrelated to the knowledge of arithmetic. It seems that the low achievers, more than the high achievers, are prone to make these types of errors, which are partly responsible for ineffective CAI work. The study concludes that the higher achieving students observed were much more able than the observed low achievers to adjust to the special environment of computer work and to derive great benefits from it.

Intelligent computer-assisted instruction: a theoretical framework

A theoretical and psychologically meaningful framework for the design of intelligent computer-assisted instruction (ICAI) systems is presented. It is argued that to design more effective and robust ICAI systems a thorough knowledge level analysis of the problem should be performed before implementation issues can be addressed. To this end, with the aid of artificial intelligence (AI) techniques and a knowledge level analysis of the problem an appropriate knowledge representation scheme and system architecture are proposed. It is further suggested that a suitable knowledge representation scheme should be psychologically valid and ideally modelled after a human tutor. One such representation, namely, Schank and Abelson's memory structures, is chosen and shown to be well matched to the requirements of an intelligent tutoring system. It models the basic memory structures of both the student and tutor upon which the goals, plans, and themes of these agents may be built. A blackboard control architecture which provides an extremely flexible environment for intelligent systems is also chosen, and is claimed to be in agreement with ICAI knowledge level requirements. A limited example is finally detailed demonstrating the applicability of this framework to ICAI systems.

Student models: the genetic graph approach

In this paper we examine the student model component of an intelligent computer-assisted instruction (ICAI) system. First, we briefly discuss the desirable capabilities of the student model and then describe, in detail, one approach to student modelling which is based on Goldstein's genetic graph. We expand Goldstein's definition and test it's feasibility in new domains, since his original domain was a limited, straightforward adventure game. In addition to modelling two diverse domains, subtraction and ballet, we also discuss the role of certain ICAI components in generating and maintaining the genetic graph.

Sigal's Ineffective Computer-Based Practice of Arithmetic: A Case Study

Sigal, a second-grade girl in Israel, was observed for several months while practicing arithmetic with a structured and controlled computer-assisted instruction (CAI) system. Sigal was average in arithmetic in her class, enjoyed the computer practice, and believed that it helped her in arithmetic. However, observations showed that she consistently failed to solve problems on the computer that she could do successfully with paper and pencil. She was therefore given CAI practice well below her real arithmetic ability. She often oscillated among a few types of exercises, sometimes regressing rather than advancing upward in the hierarchical levels of practice. Several kinds of interventions did not bring about substantial improvement in her work with the computer. Continued observations in the following 2 years showed almost no change in her CAI-related behavior. This paper suggests the reasons for Sigal's problems with the CAI practice and draws implications for CAI design and implementation.

TEACHMET: An Expert System for Teaching Weather Forecasting

Abstract Students or weather forecasting need to learn to identify efficiently the information relevant to the elements they predict. One way students learn these skills is by engaging in discussions of interesting weather situations as they occur. In these discussions, the mentor explains his reasoning in developing the forecast, and his rationale for using particular data. This process is very time consuming and relies on the student's willingness to ask questions. In this project, we developed a computer-assisted instructional system that interactively guides the student of weather forecasting in choosing and interpreting the wide variety of available data. The system is capable of giving detailed responses to the student's questions and is available whenever the student wishes to use it. The system incorporates the expertise of several faculty members and graduate students, as well as two National Weather Service forecasters. A description is given of the creation and evaluation of the system as well ...

Computer-Based Drill and Practice in Arithmetic: Widening the Gap between High- and Low-Achieving Students

Precollege schools use their computers predominantly for drill and practice in arithmetic. It has been shown by research to be the most effective application of CAI (Computer-Assisted Instruction). The present study used naturalistic methods to examine differential effectiveness of CAI in arithmetic for high- and low-achieving students. Seven elementary school students with varied backgrounds with regard to socioeconomic status (SES), grade level, type of school, and achievements in arithmetic participated in the study. The study identifies strategies that students use to advance effectively through the levels of the CAI practice with material not yet studied in class. The study also discusses software- and hardware-related errors that students make while working with the CAI system. In many cases, these errors are unrelated to the knowledge of arithmetic. It seems that the low achievers, more than the high achievers, are prone to make these types of errors, which are partly responsible for ineffective CAI work. The study concludes that the higher achieving students observed were much more able than the observed low achievers to adjust to the special environment of computer work and to derive great benefits from it.

TUTOR301-a successful application of CAI for electrical engineering problem solving

A computer-assisted instruction (CAI) system was developed to teach electrical engineering problem solving at West Point, in an introductory course for nonengineers. The system, known as TUTOR301, takes the place of an individual tutor or coach in problem-solving methods. The simple form of solution text used minimized faculty time required to prepare solution scripts for the computer. The system's basic solution structure and program structure are described along with results gathered from five semesters of using this system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>