John P. ChinPh.D Psychology, University of Maryland, College Park, MD
Personal Homepage:
linkedin.com/in/johnpchin Human Factors Professional with extensive experience in user interface design, systems engineering and testing with strong emphasis in telecommunications. Experienced in taking a product through the entire product life cycle from concept through design, implementation, testing and launch. Skilled in organizing, leading, teaming with others to generate solutions to complex processes and technologies by consistently meeting or exceeding goals and objectives.
Publications by John P. Chin (bibliography)
Chin, John P. (1996): Personality Trait Attributions to Voice Mail User Interfaces. In: Proceedings of the ACM SIG CHI April 13-18, 1996, Vancouver, BC, Canada. pp. 248-249.
“Both novices and experts ascribed similar personality traits to voice mail user interfaces: practical, intelligent, courteous, efficient, straight-forward, sophisticated, moethodical, progressive and alert. Surprisingly significantly more experts desired a more imaginative personality than novices. Results suggest that future voice mail user interfaces should project an imaginative quality.”
© All rights reserved Chin and/or his/her publisher
Chin, John P. (1993): A human factors study of speech tone delays.. In: 14th International Symposium: Human Factors in Telecommunications May 11-14, 1993, Darmstadt, Germany. p. 445.
Chin, John P., Herring, Richard D. and Familant, M. Elliott (1992): A Usability and Diary Study Assessing the Effectiveness of Call Acceptance Lists. In: Proceedings of the Human Factors Society 36th Annual Meeting 1992. pp. 216-220.
“Nuisance or unwanted calls have always been a problem to subscribers of phone services. One possible solution is a network based service that allows subscribers to control the calls they receive by using a call acceptance list. When the call acceptance list is activated, all callers not on the list would be automatically routed to a voice messaging system. Those callers on the list would be allowed to ring the subscriber's telephone. This study assessed the effectiveness of call acceptance lists in reducing unwanted telephone calls. Participants used a prototype telephone-based interface to establish a list of telephone numbers from which they would always accept calls. At the same time, they logged each of their incoming calls in a diary, recording the telephone number that originated the call, and whether they wished to receive the call. The call acceptance list significantly reduced the number of unwanted calls”
© All rights reserved Chin et al. and/or Human Factors Society
Chin, John P. (1992): A Human Factors Study of Speech Tone Delays.. In: Proceedings of the American Voice Input-Output Society AVIOS92 September 22-24, 1992. pp. 349-356.
Norman, Kent L. and Chin, John P. (1989): The Menu Metaphor: Food for Thought. In Behaviour and Information Technology, 8 (2) pp. 125-134.
“Menu selection in human/computer interaction is a metaphor of the restaurant menu. Although menu selection is widely used, its scope is currently limited, ill-defined, and information lean. A comparison of the restaurant menu and the computer menu reveal three avenues of improvement in menu systems. The correspondence of elements and features between restaurant and computer menus suggests that this powerful metaphor should be more fully developed. Second, there are a number of advantages of dynamic computer menus over static listings common to restaurants. Finally, restaurant menus currently have the advantage of breadth, richness, and graphic layout as well as a natural support system (the server) that is unparalleled in current computer applications. An analysis of deficiences in computer menus should prove invaluable in developing the next generation of menu selection techniques.”
© All rights reserved Norman and Chin and/or Taylor and Francis
Chin, John P. (1989): A Dynamic User-Adaptable Menu System: Linking it All Together. In: Proceedings of the Human Factors Society 33rd Annual Meeting 1989. pp. 413-417.
“Creation and traversal of links in a user adaptable menu was examined for syntagmatically and paradigmatically related targets. One group searched for paradigmatic related targets within the same intermediate category under different superordinate categories, while another searched for syntagmatic related targets belonging to different intermediate categories under the same superordinate. Users with syntagmatic targets created and traversed more superordinate category links, while users with paradigmatic targets traversed more intermediate category links. As predicted, more horizontal links at the same hierarchical level were created and traversed than diagonal links joining different levels. Overall, users tended to create links forming hierarchical networks.”
© All rights reserved Chin and/or Human Factors Society
Chin, John P., Diehl, Virginia A. and Norman, Kent L. (1988): Development of an Instrument Measuring User Satisfaction of the Human-Computer Interface. In: Soloway, Elliot, Frye, Douglas and Sheppard, Sylvia B. (eds.) Proceedings of the ACM CHI 88 Human Factors in Computing Systems Conference June 15-19, 1988, Washington, DC, USA. pp. 213-218.
“This study is a part of a research effort to develop the Questionnaire for User Interface Satisfaction (QUIS). Participants, 150 PC user group members, rated familiar software products. Two pairs of software categories were compared: 1) software that was liked and disliked, and 2) a standard command line system (CLS) and a menu driven application (MDA). The reliability of the questionnaire was high, Cronbach's alpha=.94 The overall reaction ratings yielded significantly higher ratings for liked software and MDA over disliked software and a CLS, respectively. Frequent and sophisticated PC users rated MDA more satisfying, powerful and flexible than CLS. Future applications of the QUIS on computers are discussed.”
© All rights reserved Chin et al. and/or ACM Press
Norman, Kent L. and Chin, John P. (1988): The Effect of Tree Structure on Search in a Hierarchical Menu Selection System. In Behaviour and Information Technology, 7 (1) pp. 51-65.
“Search processes in a hierarchical menu selection system were investigated in a study that varied the structure of the tree. A hierarchical data base was composed of 256 gift items grouped into different clusters and presented using menus. Depth of the tree was held constant while breadth varied with level. Five structures were explored with the following number of alternatives at each of four levels: constant (4 x 4 x 4 x 4), decreasing (8 x 8 x 2 x 2), increasing (2 x 2 x 8 x 8), concave (8 x 2 x 2 x 8), and convex (2 x 8 x 8 x 2). Subjects searched for either specifically named gifts (explicit targets) or gifts appropriate for a scenario situation (scenario targets). In general, explicit targets took less time to find and fewer frames to traverse than for scenario targets. For explicit targets, the increasing menu was slightly superior to the rest. Search time was about the same across the five tree structures for explicit targets, but differed greatly for scenario targets. The concave and increasing structures were faster than the constant structure and the convex and decreasing structures were slower. Similar results were found for the number of frames traversed. The patterns of search also differed greatly among the five structures in terms of the frequency of use of the 'previous' command and the 'top' command. The 'previous' command was used most frequently with the convex menu and least frequently with the concave menu. For scenario targets, the 'top' command was used most frequently with the decreasing menu and least frequently with the increasing menu. For explicit targets, the 'top' command was used most frequently with concave menu and least frequently with increasing menu. The pattern of search indicated that if subjects moved back up the tree they tended to move to a level with eight choices rather than two. Overall, it is concluded that the concave menu is superior when searching for scenario targets and the increasing menu is slightly superior when searching for explicit targets. A theory of menu uncertainly based on information theory is proposed which helps to account for some of the results.”
© All rights reserved Norman and Chin and/or Taylor and Francis
Show this list on your homepage
Knowledge wants to be free !
We have decided to give away world-class educational materials
because we believe that universal access to high quality education is key to the building
of peace, sustainable social and economic development, and intercultural dialogue.
To calculate just have much we have saved you, our wonderful readers, we compare our free encyclopedia to two
books we love:
$110: Human-Computer Interaction by Dix et al (a great textbook but without video interviews)
$116: Shneiderman's Designing the User Interface
(a great textbook but without video interviews).
As you are reading our encyclopedia on your iPad/tablet (and saving a few trees), we estimate that the price would be $90 if sold as an eBook.
With that number, we can calculate how much money we have saved our readers, based on calculating the number of readers.
How we calculate readership
Because of our online and tablet/iPad approach to publishing, we are able to precisely measure reading behaviour across hundreds of parameters in realtime: Anything from reading
speed, drop-off points in the text, reader demographics, and much more.
Based on our server logs and the Google Analytics API,
we calculate the number of readers as described in the calculation method below.
A reader is not the same as a simple pageview and a reader is not the same as a
website visitor (as described in our calculation method below).
We calculate readership for two types of readers:
- Readers that have read our whole encyclopedia, much the same way you read a printed book
- Readers that have reader an individual chapter
Calcalution method: How we define a reader
- First we use the Google Analytics API to get a report of the number of unique human visitors to a chapter/page. Google runs its business on ads and thus completely relies on the ability to distinguish between a human visitor and an automated request. If not, you could earn millions on automating clicks on Google Ads.
- We then compare that number to our Apache webserver logs, which report the much higher number of actual visits to a chapter/page (both human and automated). We calculate the difference in percent, which we call an "exaggeration factor", which we use in step 6 below.
- With a large part of the visitors excluded, we further exclude any visitor who:
- has not remained on the page for at least 3 minutes (this factor is calculated by recording visit durations of 1000 randomly selected visitors) or has not printed the page (i.e. has not visited the printerfriendly version of the chapter/page)
- has not scrolled the page (this factor is calculated by recording scroll movements on 1000 randomly selected visitors)
- We then further exclude "double readers", i.e. readers who read a portion of a chapter and then returns in,
say, a week or a month to read the rest.
Although this person's reading activity spans multiple server sessions, the person is only counted as a single reader.
We categorize a "double reader" as a visitor who:
- visits a page, or multiple pages, across multiple server sessions
- qualifies to be defined as a reader, cf step 1-3 above, in all server sessions
- uses the same originating IP address
- We then subtract 5% from the final number to counter-balance a last remaining factor, namely the situation where one reader reads a chapter on his/her tablet
using a WiFi connection (and counted as one reader) but then picks up his other tablet using a 3G dongle
(with another IP address) and re-reads some of the chapter. That will equal two readers, not one. We have no way
of calculating how many times this situation arises, but to be on the safe side we subtract 5%
from the final number.
- We then take half of the "exaggeration factor" from step 2 and substract from the final number. We do this for no rational reason. We do it only as a further measure to be certain that our number of readers is not inflated.
- To qualify as a reader who has read our whole encyclopedia - much the same way you read a printed book - that person must have qualified as a reader (cf. 1-6 above) of at least 80% of the encyclopedia chapters.
As a result, we have eliminated everything from automated requests to the more casual visitors. That leaves us with what we can safely call readers.
Changes to this page (author)
27 Feb 2010: Enabled abstracts to be shown on John P. Chin's author page.06 Jun 2008: Conference Article was added to the page (approved by an editor)06 Jun 2008: Author was added to the bibliography (approved by an editor)
06 Jun 2008: Conference Article was added to the page (approved by an editor)
06 Jun 2008: Author was added to the bibliography (approved by an editor)
06 Jun 2008: Article in Journal/Periodical was added to the page (approved by an editor)
06 Jun 2008: Author was added to the bibliography (approved by an editor)
02 Jun 2008: Page was edited
02 Jun 2008: Conference Article was added to the page (approved by an editor)
02 Jun 2008: Author was added to the bibliography (approved by an editor)
26 Jun 2007: Author was edited
26 Jun 2007: Author was edited
28 Apr 2003: Added the author to the bibliography
Page Information
Page maintainer:
The Editorial TeamHow to cite/reference this page
URL: http://www.interaction-design.org/references/authors/john_p__chin.html
