東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Using Data to Understand How Audiences Engage with Interactive Media.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using Data to Understand How Audiences Engage with Interactive Media./
作者:	Carlton, Jonathan.
面頁冊數:	1 online resource (123 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
Contained By:	Dissertations Abstracts International84-05A.
標題:	Cameras. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30159510click for full text (PQDT)
ISBN:	9798352990599

Using Data to Understand How Audiences Engage with Interactive Media.
Carlton, Jonathan.

Using Data to Understand How Audiences Engage with Interactive Media. - 1 online resource (123 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: A.

Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2022.

Includes bibliographical references

Media is evolving from traditional linear narratives to personalised experiences, where control over information (or how it is presented) is given to individual audience members. Measuring and understanding audience engagement with this media is important: a post-hoc understanding of how engaged audiences are with the content will help production teams learn from experiences and improve future productions. Engagement is typically measured by asking samples of users to self-report, which is time consuming and expensive. In some domains, however, interaction data have been used to infer engagement. The nature of interactive media facilitates a much richer set of interaction data than traditional media; this thesis aims to understand if these data can be used to understand and infer audience engagement and, by extension, the abandonment of content.This thesis reports studies, run in collaboration with the BBC, of engagement and abandonment using data captured from audience interactions with an interactive TV show and an adaptive tutorial. It was found that engagement can be modelled and predicted in the interactive TV show, and that users appear to behave differently based on their level of engagement. For example, high engagement is associated with consumption-type behaviours, while low engagement is associated with skipping-type behaviours. When investigating the data collected from the adaptive tutorial, the results revealed that user context, rather than user interactions, affects the engagement of users. Abandonment was investigated using a wider dataset collected from the national release of the interactive TV show; it was demonstrated that abandonment could be accurately predicted from the interactions of users. An increase in moving backwards and forwards in the show were indicative of an increase in abandonment, suggesting an exploratory-type behaviour. When exploring the link between abandonment and engagement, it was found that low engagement users were predicted to drop out further from the end, suggesting a relationship between the two. The results demonstrate that interaction data is a viable method for the evaluation of media is this evolving domain.To move towards consistency in the interaction data analysis field, the thesis proposes a framework to provide methodological support for researchers. Through an analysis of the literature, meta-issues were identified in the communication of research which create barriers in reproducibility and reduces transparency. The framework provides structure for those undertaking research on understanding users through their interactions and a terminology that can be applied consistently across the different disciplines in this area. It is conjectured that using such a framework should improve both the quality of science and science communication in the area, with more reproducible and transparent research being enabled.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352990599Subjects--Topical Terms:

524039
Cameras.
Index Terms--Genre/Form:

542853
Electronic books.

Using Data to Understand How Audiences Engage with Interactive Media.
LDR:04218nmm a2200349K 4500 001 2364257
005 20231130104214.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798352990599
035 $a (MiAaPQ)AAI30159510
035 $a (MiAaPQ)Manchester_UK71087240-85b1-4c5a-ab19-58abbe73c11b
035 $a AAI30159510
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Carlton, Jonathan. $3 3705063
245 1 0 $a Using Data to Understand How Audiences Engage with Interactive Media.
264 0 $c 2022
300 $a 1 online resource (123 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
500 $a Advisor: Jay, Caroline.
502 $a Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2022.
504 $a Includes bibliographical references
520 $a Media is evolving from traditional linear narratives to personalised experiences, where control over information (or how it is presented) is given to individual audience members. Measuring and understanding audience engagement with this media is important: a post-hoc understanding of how engaged audiences are with the content will help production teams learn from experiences and improve future productions. Engagement is typically measured by asking samples of users to self-report, which is time consuming and expensive. In some domains, however, interaction data have been used to infer engagement. The nature of interactive media facilitates a much richer set of interaction data than traditional media; this thesis aims to understand if these data can be used to understand and infer audience engagement and, by extension, the abandonment of content.This thesis reports studies, run in collaboration with the BBC, of engagement and abandonment using data captured from audience interactions with an interactive TV show and an adaptive tutorial. It was found that engagement can be modelled and predicted in the interactive TV show, and that users appear to behave differently based on their level of engagement. For example, high engagement is associated with consumption-type behaviours, while low engagement is associated with skipping-type behaviours. When investigating the data collected from the adaptive tutorial, the results revealed that user context, rather than user interactions, affects the engagement of users. Abandonment was investigated using a wider dataset collected from the national release of the interactive TV show; it was demonstrated that abandonment could be accurately predicted from the interactions of users. An increase in moving backwards and forwards in the show were indicative of an increase in abandonment, suggesting an exploratory-type behaviour. When exploring the link between abandonment and engagement, it was found that low engagement users were predicted to drop out further from the end, suggesting a relationship between the two. The results demonstrate that interaction data is a viable method for the evaluation of media is this evolving domain.To move towards consistency in the interaction data analysis field, the thesis proposes a framework to provide methodological support for researchers. Through an analysis of the literature, meta-issues were identified in the communication of research which create barriers in reproducibility and reduces transparency. The framework provides structure for those undertaking research on understanding users through their interactions and a terminology that can be applied consistently across the different disciplines in this area. It is conjectured that using such a framework should improve both the quality of science and science communication in the area, with more reproducible and transparent research being enabled.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Cameras. $3 524039
650 4 $a Success. $3 518195
650 4 $a Audiences. $3 618475
650 4 $a User behavior. $3 3564661
650 4 $a Demographics. $3 3540640
650 4 $a Buttons. $3 3690718
650 4 $a Television programs. $3 605397
650 4 $a Customization. $3 3682566
650 4 $a Reproducibility. $3 3683754
650 4 $a Interactive media. $3 3683965
650 4 $a Survival analysis. $3 3566266
650 4 $a Demography. $3 614991
650 4 $a Mass communications. $3 3422380
650 4 $a Sociology. $3 516174
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0938
690 $a 0708
690 $a 0626
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Manchester (United Kingdom). $3 3422292
773 0 $t Dissertations Abstracts International $g 84-05A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30159510 $z click for full text (PQDT)