Director, Institute for Research, Information Systems (IRIS); ConocoPhillips Chair of Management of Technology, and Regents Professor of Management Science and Information Systems, College of Business Administration, Oklahoma State University
The dramatic increase in distance learning (DL) enrollments in higher education is likely to continue. However, research on DL, which includes psychomotor, cognitive, and affective skills, is virtually nonexistent. Indeed, DL for psychomotor skills has been viewed as impossible. Laboratory coursework, which we define as including the acquisition of psychomotor, cognitive, and affective skills, has become a limiting factor in the growth of DL. What is needed is a synergistic integration of technologies and human-computer interface (HCI) principles from computer-supported collaborative learning (CSCL), collaborative learning systems, and immersive presence technologies to enable achievement of psychomotor learning objectives. This paper defines the computer-supported collaborative learning requiring immersive presence (CSCLIP) research area, provides a theoretical foundation for CSCLIP, and develops an agenda for research in CSCLIP to establish a foundation for the study of this emerging area. It also briefly describes a CSCLIP-based telecommunications lab currently under development. CSCLIP is presented as a major research opportunity for information systems researchers interested in empirical research as well as technical development.
After building and validating a decision support model, the decision maker frequently solves (often many times) different instances of the model. That is, by changing various input parameters and rerunning different model instances, the decision maker develops insight(s) into the workings and tradeoffs of the complex system represented by the model. The purpose of this paper is to explore inductive model analysis as a means of enhancing the decision maker's capabilities to develop insight(s) into the business environment represented by the model. The justification and foundation for inductive model analysis is based on three distinct literatures: 1) the cognitive science (theory of learning) literature, 2) the decision support system literature, and 3) the model management system literature. We also propose the integration of several technologies that might help the modeler gain insight(s) from the analysis of multiple model instances. Then we report on preliminary tests of a prototype built using the architecture proposed in this paper. The paper concludes with a discussion of several research questions. Much of the previous MIS/DSS and management science research has focused on model formulation and solution. This paper posits that it is time to give more attention to enhancing model analysis.