John W. Coffey

Knowledge modeling for the preservation of institutional memory

After setting the stage by briefly surveying knowledge elicitation techniques, this article presents a description of an iterative approach to the elicitation and representation of organizational knowledge called PreSERVe, which stands for prepare, scope, elicit, render, and verify. The method involves an initial process of preparing for knowledge elicitation, followed by an iterative process of assessing the scope of the endeavor, knowledge elicitation and rendering, and, verification. Use of the PreSERVe method is illustrated by a case study involving work with six senior engineers at NASA Glenn Research Center (NASA GRC), Cleveland, OH, USA.

An Empirical Comparison of Methods for Eliciting and Modeling Expert Knowledge

The goal of this project in Human-Centered Computing was to apply a variety of methods of Cognitive Task Analysis (CTA) and Cognitive Field Research (CFR) to support a complete process going all the way from knowledge elicitation to leverage point identification and then to system prototyping, and also use this as an opportunity to empirically compare and evaluate the methods. The research relied upon the participation of expert, journeyman, and apprentice weather forecasters at the Naval Training Meteorology and Oceanography Facility at Pensacola Naval Air Station. Methods included Protocol Analysis, a number of types of structured interviews, workspace and work patterns analysis, the Critical Decision Method, the Knowledge Audit, Concept Mapping, and the Cognitive Modeling Procedure. The methods were compared in terms of (1) their yield of information that was useful in modeling expert knowledge, (2) their yield in terms of identification of leverage points (where the application of new technology might bring about positive change), and (3) their efficiency. Efficiency was gauged in terms of total effort (time to prepare to run a procedure, plus time to run the procedure, plus time to analyze the data) relative to the yield (number of leverage points identified, number of propositions suitable for use in a model of domain knowledge). CTA/CFR methods supported the identification of dozens of leverage points and also yielded behaviorally- validated models of the reasoning of expert forecasters. Knowledge modeling using Concept-Mapping resulted in over a thousand propositions covering domain knowledge. The Critical Decision Method yielded a number of richly-populated case studies with associated Decision Requirements Tables. Results speak to the relative efficiency of various methods of CTA/CFR, and also the strengths of each of the methods. In addition to extending our empirical base on the comparison of knowledge elicitation methods, a deliverable from the project was a knowledge model that illustrates human-centered computing in that it integrates training support and performance aiding.

Knowledge modeling and the creation of El-Tech: a performance support and training system for electronic technicians

Abstract This paper contains a description of a unique approach to the creation of an expert system to provide performance support and training for electronics technicians. The starting point for development of the system was the creation of a semantically rich knowledge model comprised of Concept Maps and other digital media. The knowledge model was used to create the inference part of the system, and then retained to furnish explanation of the inference components behaviors, and as content for training. The paper starts with a survey of relevant approaches to knowledge elicitation and modeling for performance support, and a review of other systems that have been created to assist with electronics troubleshooting. Following this, El-Tech (Electronic Technician), an expert advisory and training system that was created as part of a joint research effort with the Chief of Naval Education and Training, Pensacola, FL, is described.

LEO: A Learning Environment Organizer to Support Computer-Mediated Instruction.

This article contains a description of a network-based Learning Environment Organizer entitled LEO, which takes its impetus from the Assimilation Theory of meaningful learning [1]. LEO represents a new approach to computer-mediated augmentation of face-to-face, or hybrid courses, and a different approach to distance learning course delivery. LEO provides the learner with a graphical advance organizer for the course, links to instructional content, various completion criteria for topics, and a visual representation of student progress. The organizer is non-linear in the sense that it maps only those prerequisites that are necessary for any given topic, creating many potential paths through the material. LEO is part of a software suite entitled “CmapTools” that provides a unique method of knowledge modeling or instructional content structuring. LEO can be used to organize CmapTools-type knowledge models or any other online instructional content. This work presents a description of the softwares rationale, basic functionality, look and feel, and a discussion of an example course organizer.

Locating Software Features in a SOA Composite Application

This paper describes the use of Feature Sequence Viewer (FSV) to perform feature location in a teaching and research program suite named Open SOALab. In this context, a software feature refers to software components that provide specific functionality. The composite application encompasses a system in which hotel brokers identify rooms meeting various criteria from among several hotel chains in multiple countries, and then exchanges the necessary amount of currency, using a currency broker to get several quotes and select the best one. The currency broker in turn uses two services: an authentication service and a settlement house. The various service interfaces are exposed via WSDLs. The system, running on Apache with php and nuSOAP, uses Apache’s forensic log module and microsecond time stamps to generate data that is input into FSV which produces a browsable graphical representation of the messages in the system. FSV employs a component relevance index (pc) that is used to determine which messages are displayed within the viewer. A value of pc is computed for each message in the data set loaded into FSV. A user can raise and lower the threshold value for pc so that messages with pc values above the threshold are displayed and those below are not displayed. Three experiments of increasing complexity were performed to demonstrate the ability of this approach to extract feature messages by separating them from irrelevant messages within a SOA composite application.

Storm-LK: A Human-Centered Knowledge Model for Weather Forecasting

STORM-LK (System To Organize Representations in Meteorology-Local Knowledge) is a Human-Centered system that used the CMap Tools© software to represent the knowledge and reasoning of expert forecasters. It demonstrates the feasibility of using Concept-Mapping to generate large-scale multi-media knowledge models. STORM-LK can support knowledge preservation, distance learning and collaboration, and navigation through the data that are used in weather forecasting.

Concept Map-Based Knowledge Modeling: Perspectives from Information and Knowledge Visualization

This article explores the idea of knowledge modeling as defined at the Florida Institute for Human and Machine Cognition. The notion of knowledge modeling is described to illustrate a particular method by which concept maps might be employed to create a useful structure and organization of other information and knowledge resources. Knowledge model structuring and navigational schemes afforded by the approach are described and illustrated. An example of a knowledge model pertaining to weather forecasting on the Gulf coast of the United States is presented to illustrate these ideas. Examples of how information visualization techniques have been and might be applied to the knowledge modeling scheme are discussed. Ideas pertaining to how knowledge models might serve as learning resources are briefly presented throughout. The article concludes with additional discourse regarding specific ways in which the knowledge modeling approach might be employed to create, present, and organize effective electronic learning resources.

LEO: a concept map based course visualization tool for instructors and students

This work describes a software program named LEO, a Learning Environment Organizer that provides information and knowledge visualization capabilities to students and instructors. LEO provides a Concept Map-like interface that in enhanced with extra features for course design and delivery. As such, it serves as a meta-cognitive tool for course designers and an advance organizer for students. This article contains a description of how LEO supports information and knowledge visualization for students and instructors, in the context of courseware development and course delivery.

Maintenance of service oriented architecture composite applications: static and dynamic support

Several authors have suggested that service‐oriented architecture (SOA) applications will present new program comprehension challenges for future software maintainers. We review the literature on program comprehension for SOA and describe exploratory work on static and dynamic program analysis, using two prototype tools to aid in addressing these challenges. SOAMiner is a static search tool to extract information from XML‐structured SOA files such as Web Services Description Language, XML Schema Definition Language, and Business Process Execution Language. Feature sequence viewer is a dynamic message trace analysis tool that extracts a sequence diagram highlighting the path of messages involved in processing a particular Web service request. Several case studies were conducted involving both static and dynamic analysis. Use of these tools has helped to clarify what SOA maintainers will need to know and how software tools might help them. Results revealed various types of static and dynamic information that can be useful to maintainers of SOA composite applications. Copyright

Comment on “Retrieval Practice Produces More Learning than Elaborative Studying with Concept Mapping”

Karpicke and Blunt (Reports, 11 February 2011, p. 772) reported that retrieval practice produces greater gains in learning than elaborative studying with concept mapping and concluded that this strategy is a powerful way to promote meaningful learning of complex concepts commonly found in science education. We question their findings on methodological and epistemological grounds.