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Ludic elicitation: Using games for k...

Cao, Yan.

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Ludic elicitation: Using games for knowledge elicitation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Ludic elicitation: Using games for knowledge elicitation./
作者:	Cao, Yan.
面頁冊數:	118 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: A.
Contained By:	Dissertation Abstracts International75-11A(E).
標題:	Multimedia communications. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3583344
ISBN:	9781321147742

Ludic elicitation: Using games for knowledge elicitation.
Cao, Yan.

Ludic elicitation: Using games for knowledge elicitation. - 118 p.

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: A.

Thesis (Ph.D.)--The Pennsylvania State University, 2014.

This item must not be sold to any third party vendors.

Knowledge elicitation from human beings is important for many fields, such as decision support systems, risk communication, and customer preference studying. Traditional approaches include observations, questionnaires, structured and semi-structured interviews, and group discussions. Many publications have been studying different techniques for a variety of data elicitation tasks as well. However, few of them have considered participants' user experience in the process. One main drawback of these methods is their time consuming and labor intensive nature, because of which participants often lose their interest and attention quickly in data elicitation activities. Innovated by the success of games with a purpose in many fields such as participatory city exploration and community building, we propose to adopt a game approach for knowledge elicitation tasks.

ISBN: 9781321147742Subjects--Topical Terms:

590562
Multimedia communications.

Ludic elicitation: Using games for knowledge elicitation.
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245 1 0 $a Ludic elicitation: Using games for knowledge elicitation.
300 $a 118 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-11(E), Section: A.
500 $a Adviser: William L. McGill.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Knowledge elicitation from human beings is important for many fields, such as decision support systems, risk communication, and customer preference studying. Traditional approaches include observations, questionnaires, structured and semi-structured interviews, and group discussions. Many publications have been studying different techniques for a variety of data elicitation tasks as well. However, few of them have considered participants' user experience in the process. One main drawback of these methods is their time consuming and labor intensive nature, because of which participants often lose their interest and attention quickly in data elicitation activities. Innovated by the success of games with a purpose in many fields such as participatory city exploration and community building, we propose to adopt a game approach for knowledge elicitation tasks.
520 $a We have developed two browser-based casual games, LinkIT and SortIT, and have applied them for three knowledge elicitation applications: relation elicitation, rank elicitation, and probability elicitation. The LinkIT game elicits relations between variables/ concepts and facilitates the construction of relation network structures such as concept maps and Bayesian networks. The SortIT game presents puzzles in the form of multiple-choice questions. This format supports rank elicitation in a pairwise comparison approach. The second application of this game is probability elicitation by using probability intervals or verbal expressions. By comparing the two games with more traditional methods such as questionnaires, we have established the external validity of the games for the three knowledge elicitation tasks. Further, user experience studies conclude that the games improve user experience by forming the elicitation tasks as a play activity and making the activity more interesting, engaging, exciting, and fun. These findings provide positive support for the applications of GWAP for more knowledge elicitation tasks.
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