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.

This research explored the thesis that mental model construction can be an effective instructional strategy for mental models learning by constructing a prototype that embodies this approach. The mental model to be learned was the Internet, including the components of the Internet, their functions, and their relationships to one another. To do this, students were required to place the components of the Internet in their proper places and assemble the proper functions of each component. The model-assembly module was constructed as a computer-based training module, using Authorware authoring software. A group of 43 community college students in an Introduction to Computers course participated in a pilot test. Both the control group and the treatment group went through an instructional activity and a reassembly activity. In the instructional activity, the control group observed the model assemble itself, while the treatment group were required to drag each component to its correct location. Scores on a posttest and the reassembly activity were compared. Results indicate a better grasp of the model within the treatment group, as well as significant differences between age groups and between males and females. (MES) Reproductions supplied by EDRS are the best that can be made from the original document. Student Model Construction: An Interactive Strategy for Mental Models Learning PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY

Needs assessments are traditionally based on an optimals-actuals deficiency model that is utilized before instruction is implemented. However, in some cases an existing training program may be reassessed to determine what training needs still exist. These situations could benefit from an excess-based model, where the assessment effort is designed to identify instructional excesses as well as deficiencies. This article explains the theory and procedures for an innovative needs reassessment approach, the CODE system. The article also provides some empirical data on the potential value of the CODE process for decisions about the reallocation of instructional resources in existing training programs. Two exploratory studies were conducted that provide evidence of the validity of the CODE system: (a) a needs reassessment of a corporate training program, and (b) a medical training program reassessment.

Multimedia exploration programs have different purposes than task-oriented computer-based training (CBT) programs. The multimedia learning process may be more important than its learning product, with the student accessing a rich base of information and symbol systems in a more idiosyncratic manner. The amount and structure of information in an exploratory environment also differs from task- or objectives-based programs. These process and structure differences necessitate special types of task analysis approaches. When meeting with the subject-matter expert, the instructional designer may find it helpful to use scenarios and knowledge mapping tools, and to employ variable consultation strategies. This article outlines some tools and tactics to facilitate the knowledge elicitation process in multimedia design by facilitating communication between the designer and subject matter expert (SME).

Task Analysis Methods for Instructional Design is a handbook of task analysis and knowledge elicitation methods that can be used for designing direct instruction, performance support, and learner-centered learning environments. To design any kind of instruction, it is necessary to articulate a model of how learners should think and perform. This book provides descriptions and examples of five different kinds of task analysis methods: *job/behavioral analysis; *learning analysis; *cognitive task analysis; *activity-based analysis methods; and *subject matter analysis. Chapters follow a standard format making them useful for reference, instruction, or performance support.

A mental model is a knowledge structure composed of concepts and the relations between them. Mental models are distinct from declarative and procedural knowledge--they go beyond semantic relationships and skills acquisition and contain varied intellectual skills and knowledge. This study describes an initial investigation into the construct validity of mental models as indication of a distinct learning outcome. The study considered the relationship between mental models, declarative knowledge, concept learning, problem solving, and troubleshooting performance. College junior-level Operations Management students (n=21) were tested on their knowledge and use of spreadsheets through multiple choice and matching quizzes developed using the Pathfinder Associative Network data collection method. Study limitations included: subject mortality; generalizability; missing variables; insufficient number of subjects; invalidated instruments; and convergent validity. Appendices include: definitions of structured knowledge, construct validity, and factor analysis terms; sample questions from the declarative knowledge quiz; concept matching quiz; and SPSS factor analysis results. (Contains 55 references.) (Author/SWC) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** A Construct Validation of the Mental Models Learning Outcome Using Exploratory Factor Analysis EDUCATION EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Joseph Sheehan oo Martin Tessmer o University of South Alabama Abstract Points of view or opinions stated in this document do not necessarily represent official OERI position or policy.Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. This study was designed as an initial investigation into the construct validity of mental models. The content area selected was the use of spreadsheets. The intent was to analyze the correlation between verbal information and concept learning with Pathfinder measures of mental models acquisition. Although exploratory procedures were used for the factor analysis, the authors entered the analyses with research-based hypotheses. The expectation was for three distinct factors to emerge: one describing declarative knowledge, one conceptual knowledge and one describing structural knowledge. The data actually supported two factors, with structural knowledge variables loading on one factor and declarative knowledge on the second factor. Concept knowledge loaded on both factors; this finding is discussed in the conclusions. Introduction A mental model is a knowledge structure, composed of concepts and the relations between them (Jonassen, Beissner & Yacci, 1993; Shavelson, 1974). Mental models are distinct from declarative and procedural knowledge (Jonassen & Tessmer, 1996). Anderson (1983) postulated the first stage of the development of expertise to be one of declarative encoding (distinct from procedural skills), where the subject is committing facts to memory. Declarative knowledge provides a base upon which subsequent learning can build relationships. The distinction between declarative knowledge and procedural skills is also made in R. Gagne's (1985) learning outcome taxonomy. Mental models, however, go beyond semantic relationships and skills acquisition. Mental models are knowledge structures that contain varied intellectual skills and knowledge. Jonnasen and Tessmer (1966) in addressing the distinctions, maintain: A mental model then contain three kinds of interconnected knowledge: knowing that (declarative), knowing how (skills), and knowing why (causal principles or functions). Conditional knowledge (knowing when) may also be a part of a mental model (p. 19). (A glossary of terms associated with structural knowledge, construct validity and factor analysis is contained in Appendix A.) There are at least three important implications of mental model research that should be considered by instructional designers. First, learners form mental models, whether the designer takes that fact into account or not, and inaccurate models can impede learning (Carroll & Thomas, 1982; Rouse & Morris, 1986; Norman, 1983). Second, troubleshooting can be facilitated through construction of mental models (Rouse & Morris, 1986; Gentner & Gentner, 1983). Similarly, structured knowledge has been found to be a powerful predictor of the ability to apply content knowledge (Gomez, Hadfield, & Housner, (1996). Finally, research on expert knowledge representation indicates that structural knowledge is essential to expert performance (Chi & Glaser, 1984; Larkin, McDermott, Simon, & Simon, 1980). Tardieu, Erlich, & Gyselinck, (1992), for example, found no difference between expert and novices at the prepositional level, but significant differences at the mental model level. Although the expert must have a sufficient knowledge base to draw upon, if it is not expertly structured, it is of little advantage. Construct History In recent years, several learning taxonomies have posited mental models as a learning outcome distinct from traditional outcomes of problem solving, concepts, and rules (Jonassen & Tessmer, 1996; Royer, Ciscero & Carlo, 1993). Before any construct becomes established as an independent learning outcome however, it should first be validated. As Rouse & Morris indicate, "...using a construct such as mental models results in requirements to define and illustrate the existence of such mechanisms (1986, p. 349)." While the mental models construct becomes increasingly important, it yet remains unvalidated. Kraiger & Wenzel (in press) have developed an elaborate "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY BEST COPY AVAILABLE 363 2 M. Simonsen TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC)." construct validity process for team mental models, but it is narrowly adapted to team performance, and cannot substitute for validation of the broader construct. Thus, the mental models construct remains unvalidated. The evolution of the psychology of learning from a behaviorist to cognitive science has been in progress for at least several decades. The theoretical base in support of structural knowledge can be traced back to the early work of Bruner (Bruner, Goodnow, & Austin, 1956; Bruner, 1971); Ausubel (1960, 1968); Collins & Quillian (1969); Anderson (1974a; 1976; 1983; Anderson & Piro lli, 1984); and Rumelhart & Ortony (1977). Johnson-Laird reported that mental models as theoretical entities emerged as he wrestled with inference generation. He found the construct superior to other semantic representations for explaining meaning, comprehension and discourse (1983, p. 397). In general, these authors agree that memory structures are acquired by the learner. Mental models are a type of memory structure, distinct from skills, declarative or conceptual structures (Jonassen & Tessmer, in press). Cognitive Importance Building on the theory base that information in memory is encoded in and retrieved from a structure that preserves meaning, theorists posit mental models as powerful engines for higher order cognitive processes. The mental model enables the learner to solve problems, generate inferences, and make predictions about the system that is modeled (Johnson-Laird, 1981; Wilson & Rutherford, 1989). Figure 1 models some of those functions. Figure 1. Purpose of Mental Models.

Context is a pervasive and potent force in any learning event. Yet instructional design models contain little guidance about how to accommodate contextual elements to improve learning and transfer. This paper defines context, outlines its levels and types, specifies some pertinent contextual factors within these types, suggests methods for conducting a contextual analysis and utilizing its results for instructional design, and outlines future issues for context-based instructional design. The incorporation of a contextual approach to instruction will make our design models systemic as well as systematic.

Instructional development (ID) models are intended to serve as guides to successful ID practice. There is increasing evidence, however, that most practicing instructional designers do not strictly adhere to the prescriptions found in typical ID models. Rather, ID practice seems to be characterized by selective use of the design activities contained in the models. This study examined the relationship between the inclusion of various ID activities and the perceived success of ID projects. Based on face-to-face and telephone interviews with 40 practicing designers involved in 77 ID projects, it appears that designers' perceptions of project success are a function of neither how many ID activities (derived from typical ID models) are completed, nor how thoroughly the activities are completed.

Instructional design, as it is traditionally conceptualized, is being challenged on several fronts, through surveys of design practice, cross-disciplinary studies of design practice, and practical criticism focusing on issues of design models' flexibility and efficiency. Collectively these challenges raise questions about the viability of the instructional design enterprise. This paper reviews these challenges and attempts to respond by offering alternative approaches to instructional design.

Performance + InstructionVolume 33, Issue 7 p. 3-8 Article Evaluating computer-based training for repurposing to multimedia: A case study Martin Tessmer, Martin Tessmer Martin Tessmer is professor of Instructional Design at the University of South Alabama. He completed his PhD in instructional design at Florida State University in 1982. Over the past dozen years, he has worked as an instructional design consultant to postsecondary administration and faculty at four different institutions. He has participated in over 100 instructional design projects. He is the author of Planning and Conducting Formative Evaluations and has co-author three other books. He can be reached at room 3105, University Commons, University of South Alabama, Mobile AL 36688. Telephone 205/380-2864; fax 205/380-2758; mtessmer@jaguarl.usouthal.edu is his Internet address.Search for more papers by this authorDavid Jonassen, David Jonassen David Jonassen is Professor of Instructional Technology at Penn State University. He has authored numerous books and articles on a wide range of instructional technology topics, including the recently released Structural Learning (with Barbara Grabowski) and the Handbook of Task Analysis Procedures (with Martin Tessmer and Wallace Hannum). His specialties are multimedia, hypermedia, structural learning, and constructivism.Search for more papers by this author Martin Tessmer, Martin Tessmer Martin Tessmer is professor of Instructional Design at the University of South Alabama. He completed his PhD in instructional design at Florida State University in 1982. Over the past dozen years, he has worked as an instructional design consultant to postsecondary administration and faculty at four different institutions. He has participated in over 100 instructional design projects. He is the author of Planning and Conducting Formative Evaluations and has co-author three other books. He can be reached at room 3105, University Commons, University of South Alabama, Mobile AL 36688. Telephone 205/380-2864; fax 205/380-2758; mtessmer@jaguarl.usouthal.edu is his Internet address.Search for more papers by this authorDavid Jonassen, David Jonassen David Jonassen is Professor of Instructional Technology at Penn State University. He has authored numerous books and articles on a wide range of instructional technology topics, including the recently released Structural Learning (with Barbara Grabowski) and the Handbook of Task Analysis Procedures (with Martin Tessmer and Wallace Hannum). His specialties are multimedia, hypermedia, structural learning, and constructivism.Search for more papers by this author First published: August 1994 https://doi.org/10.1002/pfi.4160330703Citations: 7 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume33, Issue7August 1994Pages 3-8 RelatedInformation

Traditional formative evaluation methods and tools have a documented history of successful use. Expert, one-to-one, small group, and field-test methods have been used for decades. However, changes in design practice, technology, and lately, in theory, have resulted in alternative evaluation methods that are not as well-known in current design literature. These methods can be used to complement or replace traditional evaluation methods in a formative evaluation project. Some of these alternative methods use different groupings of experts or learners, while others use different evaluation questions or technologies. This article explains each of these formative evaluation alternatives and outlines their advantages, disadvantages, and applicable contexts.