The Strategic Use of Complex Computer Systems
Abstract
Several studies show that despite experience, many users with basic command knowledge do not progress to an efficient use of complex computer applications. These studies suggest that knowledge of tasks and knowledge of tools are insufficient to lead users to become efficient. To address this problem, we argue that users also need to learn strategies in the intermediate layers of knowledge lying between tasks and tools. These strategies are (a) efficient because they exploit specific powers of computers, (b) difficult to acquire because they are suggested by neither tasks nor tools, and (c) general in nature having wide applicability. The above characteristics are first demonstrated in the context of aggregation strategies that exploit the iterative power of computers. A cognitive analysis of a real-world task reveals that even though such aggregation strategies can have large effects on task time, errors, and on the quality of the final product, they are not often used by even experienced users. We identify other strategies beyond aggregation that can be efficient and useful across computer applications and show how they were used to develop a new approach to training with promising results. We conclude by suggesting that a systematic analysis of strategies in the intermediate layers of knowledge can lead not only to more effective ways to design training but also to more principled approaches to design systems. These advances should lead users to make more efficient use of c…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In computer-aided design, long-term users often employ inefficient strategies for completing tasks.
“field studies demonstrated that long-term users often employ inefficient strategies for completing tasks [Bhavnani and John 2000]”
From Supporting Novice to Expert Transitions in User Interfaces (2015) · cites this paper as Bhavnani and John 2000
Cites
The GOMS family of user interface analysis techniques: comparison and contrast
John, B. E., & Kieras, D. E. (1996) · ACM Transactions on Computer-Human Interaction · doi:10.1145/235833.236054 · Cited by 687 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Supporting Novice to Expert Transitions in User Interfaces
Cockburn, A., Gutwin, C., Scarr, J., & Malacria, S. (2015) · ACM Computing Surveys · doi:10.1145/2659796 · Cited by 193 · see citing works
A framework for analyzing the cognitive complexity of computer-assisted clinical ordering
Horsky, J., Kaufman, D. R., Oppenheim, M. I., & Patel, V. L. (2003) · Journal of Biomedical Informatics · doi:10.1016/s1532-0464(03)00062-5 · Cited by 137 · see citing works
- Authors
- Bhavnani, S. K., & John, B. E.
- Affiliations
- University of Michigan (US)
- Carnegie Mellon University (US)
- Venue
- Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2000-09
- Volume
- 15
- Issue
- 2-3
- Pages
- 107-137
- ISSN
- 0737-0024 (ISSN-L)
- 1532-7051
- OpenAlex
- W1971690354
- Cited by
- 82
- References
- 1
- Citations by year
- 2024: 2 · 2022: 1 · 2020: 3 · 2019: 2 · 2018: 5 · 2017: 2 · 2016: 2 · 2015: 3 · 2014: 4 · 2013: 5 · 2012: 7
- FWCI
- 7.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human-Computer Interaction (publishedVersion)
- Cumulative Index of Computer Aided Architectural Design (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- user strategies, aggregation methods, iterative computation, user training, training program design, user efficiency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review