Student Construction of Expert Systems in the Classroom

Knox-Quinn, Carolyn

doi:10.2190/dpec-h5q4-7b0x-61wm

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Lippert (1988) found that physics students who developed rule bases to solve problems about forces reported meaningful learning from evaluating their own thought processes, more enthusiasm for learning, and the learning of content that they were not expected to master. Knox-Quinn (1992) reported that MBA students who developed knowledge bases on tax laws were consistently engaged in higher-order thinking, such as classifying information, breaking down content, organizing information, and integrating and elaborating information, and students who built expert systems reasoned more similarly to experts. Mason-Mason (1999) found that creating expert systems substantially increased participants' scores on three measures of mental model development.…”

Section: Expert Systems As Mindtoolsmentioning

confidence: 99%

Transfer Effects of Semantic Networks on Expert Systems: Mindtools at Work

Marra

Jonassen

2002

Journal of Educational Computing Research

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Semantic networks and expert systems can support learning and critical thinking as Mindtools. Mindtools are computer-based tools that function as intellectual partners with the learner in order to engage and facilitate critical thinking and higher-order learning. Semantic networks and expert systems in particular are cognitive reflection tools that help learners to build a representation of what they know by designing their own knowledge bases. Semantic networks have been used as a knowledge elicitation tool for expert system construction; however, the effects of using these tools together has never been formally studied. This study examined the effects of building semantic networks on the coherence and utility of expert systems subsequently constructed. Subjects who constructed semantic networks first produced expert systems with significantly more rules and rule types than a control group. The task's intentional ambiguity and the differences in thinking necessary for semantic network and expert system construction may have affected the non-significance of other expert system complexity variables. INTRODUCTION: COMPUTERS AS MINDTOOLSInstructional technologies have traditionally been used as conveyors of information and tutors of students. In these traditional applications, information is stored in the computer. During the "instructional" process, learners perceive the messages stored in the computers and "interact" with the technology. In contrast, Derry and La Jole (1993) argue that "the appropriate role for a computer system is not that of a teacher/expert, but rather, that of a mind-extension 'cognitive 1  2002, Baywood Publishing Co., Inc.

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Section: Expert Systems As Mindtoolsmentioning

confidence: 99%

Transfer Effects of Semantic Networks on Expert Systems: Mindtools at Work

Marra

Jonassen

2002

Journal of Educational Computing Research

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“…These descriptive studies, principally in the realm of problem solving, found that students who created expert systems routinely processed information to a greater depth and in greater detail than students who had previously learned by other techniques (Jonassen et al, 1993;Knox-Quinn, 1995;Lai, 1989;Law & Ogborn, 1994;Odom & Pourjalali, 1996). Expert system studies have investigated the depth of processing by students as a result of creating expert systems on a variety of subjects.…”

Section: Problem Summarymentioning

confidence: 99%

“…Learning gains resulting from student-constructed expert systems are well documented in the literature, however those studies measured outcomes other than mental models, such as problem solving (Lai, 1989;Law, 1995;Law & Ogborn, 1994;Lippert, 1988;Tamashiro & Bechtelheimer, 1991), engaging students in deeper processing of knowledge (Kieras & Boy-air, 1984), and organizing information more logically (Bouwman & Knox-Quinn, 1995;Knox-Quinn, 1995;Willie, 1990), as a result of learning by constructing expert systems. The present study's results suggest agreement with those findings, and in addition, may possibly imply that learning gains can be assessed by measuring the change in participants' mental models through several complementary measures.…”

Section: Recommendationsmentioning

confidence: 99%

Expert systems as a mindtool to facilitate mental model learning

Mason-Mason

2000

ETR&D

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This exploratory study investigated the question, Does the process of constructing an expert system model of a hydraulic drum braking system promote the formation of expert-like mental models ? Thirty-three participants in three countries, who reported no knowledge of the subject domain in which the expert system was to be created, read encyclopedia extracts, viewed graphics, and then created small expert systems. The study was conducted via mainframe-mediated network using a variety of communication protocols.Participants" mental models were assessed using Pathfinder concept networks technique, troubleshooting tests, and prediction (change of state) tests. Each of the three measures was administered three times, as pretests, midtests, and posttests. Scores on all three measures suggested that there were small increases from pretest to midtest after participants studied the brake text and graphics. There were substantially larger increases in all scores from midtest to posttest when the expert system was constructed.In poststudy surveys and interviews participants responded that their knowledge in the subject domain had increased, that the creation of the expert system had been very helpful in learning the material, and that they had had fun doing it. When used as a mindtool, an expert system becomes a process, not the end product. It can facilitate a learning gain by engaging students,fostering their concentration, and assisting them in organizing systemic information.

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“…The research studies that do exist are based on anecdotal data and observation of few subjects and were conducted over short periods of time. They include subjects in a variety of areas --college students in physics (Trollip andLippert 1987, Lippert 1988), high school students in biology (Wideman and Owston 1988), nursing students studying alcoholism (Lai 1989), and MBA students (Knox-Quinn 1992). The research indicates that the construction of expert systems engages students in problem solving at a level that requires the use of new or expanded cognitive strategies just beyond what they currently possess.…”

Section: Previous Research On Learner-generated Expert Systemsmentioning

confidence: 99%

Enhancing Statistics Education with Expert Systems: More than an Advisory System

Barbara

William

1996

Journal of Statistics Education

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This article reports on the results of two studies that investigated the effectiveness of different uses of expert systems in large introductory statistics classes. Three groups of students were compared-those who used an expert system created by the instructor of the course, those who created their own expert system, and those who did not use any at all. The first experiment showed non-significant, but interesting, trends that were explored in the second experiment. In the second experiment, significant differences emerged as the semester evolved in favor of those who used the expert system, regardless of whether or not the students created it themselves. These differences disappeared on the final exam, when technological problems added to the end-of-the-semester tension. These findings support the notion that the use of expert systems in the classroom can have an important impact on the level and amount of learning that occurs. This article describes these two studies in detail and draws some implications for teaching.

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Student Construction of Expert Systems in the Classroom

Cited by 5 publications

References 7 publications

Transfer Effects of Semantic Networks on Expert Systems: Mindtools at Work

Transfer Effects of Semantic Networks on Expert Systems: Mindtools at Work

Expert systems as a mindtool to facilitate mental model learning

Enhancing Statistics Education with Expert Systems: More than an Advisory System

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