Toward a Human Centered Scientific Problem Solving Environment

This paper reviews human computer interaction considerations useful in developing scientific problem solving environments. First we argue for the importance of understanding user goals and task-oriented needs and then draw a distinction between interface and interaction design, arguing that both levels must be addressed in human centered design. Next we discuss the role of analogical thinking in the development of scientific knowledge and consider implications of analogical thinking for the design of problem solving environments. Finally, we conclude with recommendations for characteristics of an ideal problem solving environment.

[1]  Lex Wolters,et al.  Tomorrow's weather forecast: automatic code generation for atmospheric modeling , 1997 .

[2]  Linda Candy,et al.  Creative design of the Lotus bicycle: implications for knowledge support systems research , 1996 .

[3]  Linda Candy,et al.  Computer support for concept engineering design: Enabling interaction with design knowledge , 1996 .

[4]  D. Norman The psychology of everyday things , 1990 .

[5]  K. Mani Chandy,et al.  Integrating task and data parallelism with the group communication archetype , 1995, Proceedings of 9th International Parallel Processing Symposium.

[6]  John R. Rice,et al.  Future Research Directions in Problem Solving Environments for Computational Science , 1991, Programming Environments for High-Level Scientific Problem Solving.

[7]  K. Holyoak,et al.  Mental Leaps: Analogy in Creative Thought , 1994 .

[8]  Allan Bonadio Mathematical User Interfaces for Graphical Workstations , 1991, Programming Environments for High-Level Scientific Problem Solving.

[9]  John R. Rice,et al.  Scientific computing via the Web: the Net Pellpack PSE server , 1997 .

[10]  Ernest A. Edmonds,et al.  End-User Manipulation of a Knowledge-Based System: A Study and an Expert's Practice , 1993, Int. J. Man Mach. Stud..

[11]  Clayton Lewis,et al.  Designing for usability—key principles and what designers think , 1983, CHI '83.

[12]  K. Dunbar HOW SCIENTISTS REALLY REASON: SCIENTIFIC REASONING IN REAL-WORLD LABORATORIES , 1995 .

[13]  E. R. Levine,et al.  A problem-solving Workbench for Interactive Simulation of Ecosystems , 1997 .

[14]  John Clement,et al.  Observed Methods for Generating Analogies in Scientific Problem Solving , 1987, Cogn. Sci..

[15]  Robert L. Young,et al.  SciNapse: a problem-solving environment for partial differential equations , 1997 .

[16]  Joan M. Ryder,et al.  Cognitive Task Analysis of Expertise in Air Traffic Control , 1993 .

[17]  Linda Candy,et al.  Supporting the creative user: a criteria-based approach to interaction design , 1997 .

[18]  C. T. Meadow,et al.  On designing for usability: an application of four key principles , 1986, CHI '86.

[19]  K. M. Chandy Concurrent program archetypes , 1994, Proceedings Scalable Parallel Libraries Conference.

[20]  Stuart I. Feldman Environments for Large-Scale Scientific Computation , 1991, Programming Environments for High-Level Scientific Problem Solving.

[21]  John D. Gould,et al.  The 1984 Olympic Message System: a test of behavioral principles of system design , 1987, CACM.

[22]  Barry Kirwan,et al.  A Guide To Task Analysis: The Task Analysis Working Group , 1992 .