Computer Coach Research Articles

Computer coach of the top team in PIRS technology

Computer coach of the top team in PIRS technology

A cross-cultural study of self-regulated learning in a computer-supported collaborative learning environment

Self-regulated learning (SRL) actions of 30 Canadian and 30 Chinese university students were studied in a face-to-face problem-based learning environment. Participants were randomly assigned to work in dyads consisting of Chinese, Canadian, or mixed Chinese–Canadian pairs to learn Analysis of Variance collaboratively using a computer coach. Dialog segments were coded in terms of types of SRL actions, and whether an action was individually (IND) or socially (SOC) oriented. Canadian pairs produced significantly higher proportions of IND-SRL actions relative to SOC-SRL actions (higher odds ratios) than Chinese pairs, but the odds ratios for mixed pairs were similar to those for Canadian pairs. This effect of cultural composition of dyads for the Canadian and Chinese pairs was replicated in a comparison of Canadian and Chinese participants within the mixed pairs dyads. These results have implications for a socio-cultural account of SRL.

The structure of the help-seeking process in collaboratively using a computer coach in problem-based learning

Research on help seeking with a computer coach providing on-demand help has not produced fully adequate models of the process from a cognitive perspective. The present study postulates a model of help seeking from a cognitive perspective and tests this model in a learning situation characterized as problem-based and computer-supported. The participants were 18 graduate students from the faculty of Education of a Canadian university. Their participation involved solving a complex problem in statistics, in pairs, with the help of a computer coach, the McGill statistics tutor. Analysis of the performance data was performed using directed probability graphs and the log-linear approach. Results show that the model is reflected in the data. Implications for the design of computer coaches and instructional situations are discussed.

Individual differences in graduate students' help-seeking process in using a computer coach in problem-based learning

There is a scarcity of research regarding help seeking in the context of computer learning environments providing on-demand help, a context in which help-seeking skills appear critical for learning [Aleven, V., Stahl, E., Schworm, S., Fisher, F., & Wallace, R. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research, 73 (3), 277–320]. Whereas a cognitive model of help seeking has been suggested and tested in this context [Mercier, J., & Frederiksen, C. H. The structure of the help-seeking process in using a computer coach in problem-based learning. Computers and Education, submitted for publication], individual differences remained to be studied from this perspective. Participants were 18 graduate students, who solved a challenging statistics problem in pairs, with the help of a computer coach providing on-demand help. Log linear analyses found individual differences in help seeking with respect to (a) its relative importance in problem solving, (b) the relative importance of its component processes, (c) the modulation of help seeking over a series of sub-tasks, and (d) the relationship between help seeking and the quality of the solutions that the student elaborated. Theoretical implications are discussed.

Developing a computer‐based peer tutoring system

The article presents an outline for the design of structured peer tutoring in a computing environment. A computer based peer tutoring system should include a presentation mode, a teaching mode and a mixed initiative mode. In this system, a student's role is reversed toward a teacher's competence after he or she gains initial competence. The student is expected to learn more toward the teacher's competence while explaining concepts with annotated descriptions and receiving feedback from hidden computer coach.

Cognitive Assessment in Coached Learning Environments

An approach to cognitive assessment of problem-solving in complex computer-based tutorial environments is described. The approach is based on studies of expert tutoring and students' performance in natural tutoring situations in specific domains such as engineering and statistics. A model of expert tutors' knowledge in a domain of applied statistics was developed and used as a basis for a web-based computer coach that emulates human tutoring. Cognitive assessments are obtained from records of students' actions as they learn to apply particular components of the procedural knozvledge required to solve problems in the domain with the help of the computer tutor. Learning is evaluated by studying changes in these records of performance as students practice successive problem exercises. These assessments can then be used subsequently to predict students' unassisted performance in solving post-instruction transfer problems.

ORIENTMAN: an intelligent tutor for the ORIENT software development methodology

The paper presents ORIENTMAN, an Intelligent Tutoring System that teaches the ORIENT methodology for object-oriented analysis and design. This methodology has already been implemented in the ORIENT CASE toolset, which was designed to support and automate the system development process using a complete (from analysis to construction-generation) object-oriented approach. ORIENTMAN has been developed using GENITOR, an ITS generator that supports the design, implementation, evaluation and revision of ITSs that specialise in methodology training. It is a stand-alone training application that combines the features of a computer coach and a gaming environment in simulation-based training. It guides the trainees through a three-stage training cycle: the first stage trains them, with the use of an expert system, in the structure of the ORIENT methodology; the second stage presents, under the control of another expert system, supporting and explanatory information on the application of the methodology; and the third stage supports the self-evaluation of the acquired skills. ORIENTMAN has already been put in use, with encouraging results, as a companion training tool that will help ORIENT users in understanding and applying the ORIENT methodology.

Modeling the teaching consultant

A model of a teaching consultant is presented. The teaching consultant, an extension of the concept of a computer coach, is concerned with filling gaps in a student's knowledge base relative to a specific application. The student model used by the consultant is stored in the form of a prerequisite network, which maintains nodes of information that are connected so that prerequisite information may be readily accessed. The Teaching Consultant Model is being used as the basis for an intelligent tutoring system serving as a programming consultant for novice programmers.

Intelligent computer-aided instruction: a survey organized around system components

The issues and previous research in intelligent computer-aided instruction (ICAI) are surveyed, concentrating on the contribution of each effort to understanding the various components of ICAI systems. Comparisons are made between ICAI and CAI. Various learning scenarios are discussed including computer coaches, gaming environments, mixed initiative dialog, Socratic tutors, articulate experts, interactive simulation, and discovery learning. Various forms of knowledge representation are discussed along with relevant issues and examples. Several techniques for student modeling and diagnosis are presented, as are their respective advantages and disadvantages. Pedagogical knowledge, its role in ICAI and several examples are highlighted. The evolution of discourse-management techniques for ICAI is outlined. Techniques for the automatic generation of problems from a general base of domain knowledge are presented. The design of user interfaces for ICAI systems is briefly discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

User modelling for a computer coach: a case study

A computer coach unobtrusively monitors interaction with a system and offers individualized advice on its use. Such active on-line assistance complements conventional documentation and its importance grows as the complexity of interactive systems increases. Instead of studying manuals, users learn highly-reactive systems through experiment, imported metaphors and natural intelligence. However, in so doing they inevitably fail to discover features which would help them in their work. This paper describes Anchises, a coach which aims to detect inefficient use and ignorance of important facilities of an interactive program in a domain-independent way. Its current knowledge base is the Emacs text editor, and Anchises provides highly-selective access to pertinent parts of the on-line documentation with little overhead for the user. In the design of Anchises, close attention has been paid to the user modelling component which determines the needs of an individual without entering into any explicit dialogue with him; in general this is the least well-understood aspect of computer coaches. An informal experiment was conducted to determine the effectiveness of the user modelling techniques employed.

Review of "Artificial Intelligence: An MIT Perspective, Volume 2: Understanding Vision, Manipulation, Computer Design, Symbol Manipulation by Patrick Henry Winston and Richard Henry Brown"; The MIT Press, Cambridge, Massachusetts, Second printing, 1980, ISBN 0-262-23097-6.

Artificial Intelligence: An MIT Perspective consists of two volumes. In my previous review of Volume I (which has sections on Expert Problem Solving, Natural Language Understanding, Intelligent Computer Coaches, and Representation and Learning) I evaluated this set from a general perspective. For brevity, I will not repeat my comments here. Instead, I refer the reader who has not seen the first part of this article to the preceding issue of the Sigart Newsletter. Nevertheless, for the anxious reader I present my point of view concisely: These two volumes make up an excellent set of references and belong to the library of every serious Al researcher.

Artificial Intelligence: An MIT Perspective. Volume 1 (Expert Problem Solving, Natural Language Understanding, Intelligent Computer Coaches, Representation and Learning) and Volume 2 (Understanding Vision, Manipulation, Computer Design,Symbol Manipulation).Patrick Henry Winston , Richard Henry Brown

Previous articleNext article No AccessNew Biological BooksArtificial Intelligence: An MIT Perspective. Volume 1 (Expert Problem Solving, Natural Language Understanding, Intelligent Computer Coaches, Representation and Learning) and Volume 2 (Understanding Vision, Manipulation, Computer Design,Symbol Manipulation). Patrick Henry Winston , Richard Henry Brown Michael A. ArbibMichael A. Arbib Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Quarterly Review of Biology Volume 55, Number 3Sep., 1980 Published in association with Stony Brook University Article DOIhttps://doi.org/10.1086/411955 Copyright 1980 Stony Brook Foundation, Inc.PDF download Crossref reports no articles citing this article.