Using predictive human performance models to inspire and support UI design recommendations

Using predictive human performance models to inspire and support UI design recommendations. John, B. E. In Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11, pages 983, Vancouver, BC, Canada, 2011. ACM Press.

Paper doi abstract bibtex

Predictive human performance modeling has traditionally been used to make quantitative comparisons between alternative designs (e.g., task execution time for skilled users) instead of identifying UI problems or making design recommendations. This note investigates how reliably novice modelers can extract design recommendations from their models. Many HCI evaluation methods have been plagued by the “evaluator effect” [3], i.e., different people using the same method find different UI problems. Our data and analyses show that predictive human performance modeling is no exception. Novice modelers using CogTool [5] display a 34% Any-Two Agreement in their design recommendations, a result in the upper quartile of evaluator effect studies. However, because these recommendations are grounded in models, they may have more reliable impact on measurable performance than recommendations arising from less formal methods.

@inproceedings{john_using_2011,
	address = {Vancouver, BC, Canada},
	title = {Using predictive human performance models to inspire and support {UI} design recommendations},
	isbn = {978-1-4503-0228-9},
	url = {http://dl.acm.org/citation.cfm?doid=1978942.1979088},
	doi = {10.1145/1978942.1979088},
	abstract = {Predictive human performance modeling has traditionally been used to make quantitative comparisons between alternative designs (e.g., task execution time for skilled users) instead of identifying UI problems or making design recommendations. This note investigates how reliably novice modelers can extract design recommendations from their models. Many HCI evaluation methods have been plagued by the “evaluator effect” [3], i.e., different people using the same method find different UI problems. Our data and analyses show that predictive human performance modeling is no exception. Novice modelers using CogTool [5] display a 34\% Any-Two Agreement in their design recommendations, a result in the upper quartile of evaluator effect studies. However, because these recommendations are grounded in models, they may have more reliable impact on measurable performance than recommendations arising from less formal methods.},
	language = {en},
	urldate = {2019-12-09},
	booktitle = {Proceedings of the 2011 annual conference on {Human} factors in computing systems - {CHI} '11},
	publisher = {ACM Press},
	author = {John, Bonnie E.},
	year = {2011},
	keywords = {Guidance, Recommendation, Performance Modeling, exclude because of missing vis context},
	pages = {983},
	file = {John - 2011 - Using predictive human performance models to inspi.pdf:C\:\\Users\\conny\\Zotero\\storage\\MXUWYHFP\\John - 2011 - Using predictive human performance models to inspi.pdf:application/pdf}
}

Downloads: 0

{"_id":"EL4gQaEwQnpsDTpoa","bibbaseid":"john-usingpredictivehumanperformancemodelstoinspireandsupportuidesignrecommendations-2011","authorIDs":[],"author_short":["John, B. E."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Vancouver, BC, Canada","title":"Using predictive human performance models to inspire and support UI design recommendations","isbn":"978-1-4503-0228-9","url":"http://dl.acm.org/citation.cfm?doid=1978942.1979088","doi":"10.1145/1978942.1979088","abstract":"Predictive human performance modeling has traditionally been used to make quantitative comparisons between alternative designs (e.g., task execution time for skilled users) instead of identifying UI problems or making design recommendations. This note investigates how reliably novice modelers can extract design recommendations from their models. Many HCI evaluation methods have been plagued by the “evaluator effect” [3], i.e., different people using the same method find different UI problems. Our data and analyses show that predictive human performance modeling is no exception. Novice modelers using CogTool [5] display a 34% Any-Two Agreement in their design recommendations, a result in the upper quartile of evaluator effect studies. However, because these recommendations are grounded in models, they may have more reliable impact on measurable performance than recommendations arising from less formal methods.","language":"en","urldate":"2019-12-09","booktitle":"Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11","publisher":"ACM Press","author":[{"propositions":[],"lastnames":["John"],"firstnames":["Bonnie","E."],"suffixes":[]}],"year":"2011","keywords":"Guidance, Recommendation, Performance Modeling, exclude because of missing vis context","pages":"983","file":"John - 2011 - Using predictive human performance models to inspi.pdf:C\\:\\\\Users\\o̧nny\\\\Zotero\\\\storage\\\\MXUWYHFP\\\\John - 2011 - Using predictive human performance models to inspi.pdf:application/pdf","bibtex":"@inproceedings{john_using_2011,\n\taddress = {Vancouver, BC, Canada},\n\ttitle = {Using predictive human performance models to inspire and support {UI} design recommendations},\n\tisbn = {978-1-4503-0228-9},\n\turl = {http://dl.acm.org/citation.cfm?doid=1978942.1979088},\n\tdoi = {10.1145/1978942.1979088},\n\tabstract = {Predictive human performance modeling has traditionally been used to make quantitative comparisons between alternative designs (e.g., task execution time for skilled users) instead of identifying UI problems or making design recommendations. This note investigates how reliably novice modelers can extract design recommendations from their models. Many HCI evaluation methods have been plagued by the “evaluator effect” [3], i.e., different people using the same method find different UI problems. Our data and analyses show that predictive human performance modeling is no exception. Novice modelers using CogTool [5] display a 34\\% Any-Two Agreement in their design recommendations, a result in the upper quartile of evaluator effect studies. However, because these recommendations are grounded in models, they may have more reliable impact on measurable performance than recommendations arising from less formal methods.},\n\tlanguage = {en},\n\turldate = {2019-12-09},\n\tbooktitle = {Proceedings of the 2011 annual conference on {Human} factors in computing systems - {CHI} '11},\n\tpublisher = {ACM Press},\n\tauthor = {John, Bonnie E.},\n\tyear = {2011},\n\tkeywords = {Guidance, Recommendation, Performance Modeling, exclude because of missing vis context},\n\tpages = {983},\n\tfile = {John - 2011 - Using predictive human performance models to inspi.pdf:C\\:\\\\Users\\\\conny\\\\Zotero\\\\storage\\\\MXUWYHFP\\\\John - 2011 - Using predictive human performance models to inspi.pdf:application/pdf}\n}\n\n","author_short":["John, B. E."],"key":"john_using_2011","id":"john_using_2011","bibbaseid":"john-usingpredictivehumanperformancemodelstoinspireandsupportuidesignrecommendations-2011","role":"author","urls":{"Paper":"http://dl.acm.org/citation.cfm?doid=1978942.1979088"},"keyword":["Guidance","Recommendation","Performance Modeling","exclude because of missing vis context"],"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/JKU-ICG/provenance-survey/master/assets/data/main.bib?token=AFMAFPFPO73YUVEBH3EL22C6YQDJU","creationDate":"2020-05-12T16:18:24.887Z","downloads":0,"keywords":["guidance","recommendation","performance modeling","exclude because of missing vis context"],"search_terms":["using","predictive","human","performance","models","inspire","support","design","recommendations","john"],"title":"Using predictive human performance models to inspire and support UI design recommendations","year":2011,"dataSources":["iLdxLZjd62Nd3rAFg"]}