Training wheels in a user interface
Open accessAbstract
New users of high-function application systems can become frustrated and confused by the errors they make in the early stages of learning. A training interface for a commercial word processor was designed to make typical and troublesome error states “unreachable,” thus eliminating the sources of some new-user learning problems. Creating a training environment from the basic function of the system itself afforded substantially faster learning coupled with better learning achievement and better performance on a comprehension post-test. A control group spent almost a quarter of their time recovering from the error states that the training interface blocked off. We speculate on how this training strategy might be refined, and more generally, on how function should be organized in a user interface.
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Carroll and Carrithers exploited the effect of reducing displayed controls with Training Wheels interfaces, which intentionally reduced the number of commands displayed.
“Carroll and Carrithers [1984] exploited this effect with their Training Wheels interfaces, which intentionally reduced the number of commands displayed.”
From Supporting Novice to Expert Transitions in User Interfaces (2015) · cites this paper as Carroll and Carrithers [1984]
The Training Wheels Interface section says the authors made only basic menu choices accessible; selecting an advanced choice displayed a message that the function was unavailable. The Discussion says menus were kept identical between the training-wheels and complete interfaces. The paper therefore describes restricting which commands worked, not reducing the number displayed. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe original concept of progressive disclosure involved hiding advanced interface controls, allowing users to make fewer initial errors and learn the system more effectively.
“The original concept involved hiding advanced interface controls; allowing users to make fewer initial errors and learn the system more effectively [15].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [15]
The Training Wheels Interface section says the design made advanced functions inaccessible and blocked troublesome error states; the Discussion reports that it facilitated learning. But the paper does not say advanced controls were hidden: users could still select an unavailable menu choice and receive a message, and the menus were kept identical to those of the complete system. The claim is supported on error protection and learning, but not on hiding controls. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe original concept of progressive disclosure involved hiding advanced interface controls, allowing users to make fewer initial errors and learn the system more effectively.
“The original concept involved hiding advanced interface controls; allowing users to make fewer initial errors and learn the system more effectively [14].”
From Progressive disclosure (2019) · cites this paper as [14]
The Training Wheels Interface section says advanced functions were blocked while basic functions remained available; the abstract reports faster learning and better achievement. But advanced menu choices were not hidden: users could select them and receive an unavailable-function message. The paper supports preventing troublesome error states, not necessarily users making fewer initial mistakes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredProgressive disclosure has a long history in UI research, dating back to the Xerox Star and early word processing systems.
“Progressive disclosure has a long history in UI research, dating back to the Xerox Star and early word processing systems [14,61,74].”
From Progressive disclosure (2019) · cites this paper as [14]
The Training Wheels Interface and Discussion sections describe a 1984 word-processor interface that made basic functions available while withholding advanced ones, and call this approach “staging” the presentation of functions. This supports the early word-processing part of the claim, but our copy does not connect the approach to the Xerox Star. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThis enhanced understanding of core concepts gives them a framework that later helps them integrate the complexities of the advanced features.
“This enhanced understanding of core concepts gives them a framework that later helps them integrate the complexities of the advanced features [6,15].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [15]
Progressive disclosure has a long history in UI research, dating back to the Xerox Star and early word processing systems.
“Progressive disclosure has a long history in UI research, dating back to the Xerox Star and early word processing systems [15,62,76].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [15]
People learn better when they are initially limited to a smaller set of core features.
“However, empirical studies examining progressive disclosure show that people learn better when they are initially limited to a smaller set of core features.”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [15]
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- Authors
- Carroll, J. M., & Carrithers, C.
- Affiliations
- IBM (United States) (US)
- Columbia University (US)
- ORCID
- Carroll, John M.
- Venue
- Communications of the ACM
- Published
- 1984-08
- Volume
- 27
- Issue
- 8
- Pages
- 800-806
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W114843461
- Cited by
- 345
- References
- 2
- Citations by year
- 2026: 5 · 2025: 57 · 2024: 5 · 2023: 8 · 2022: 7 · 2021: 2 · 2020: 5 · 2019: 5 · 2018: 8 · 2017: 3 · 2016: 7 · 2015: 6 · 2014: 10 · 2013: 17 · 2012: 5
- FWCI
- 12.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Ablex Publishing Corp. eBooks
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Applied Psychology › Human Resource Development and Performance Evaluation
- Keywords
- user interface design, novice users, user learning, error states, word processing software, user understanding
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 ›
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