Automated dialog analysis to predict blogger community response to newcomer inquiries. (December 2018)
- Record Type:
- Journal Article
- Title:
- Automated dialog analysis to predict blogger community response to newcomer inquiries. (December 2018)
- Main Title:
- Automated dialog analysis to predict blogger community response to newcomer inquiries
- Authors:
- Nistor, Nicolae
Dascalu, Mihai
Serafin, Yvonne
Trausan-Matu, Stefan - Abstract:
- Abstract: Informal learning in online knowledge building communities (OKBCs) often starts with online academic help seeking, and with visitor inquiries on specific topics. In such a context, learning presupposes adequate OKBC response. Employing a social learning analytics approach based on natural language processing and Bakhtin's theory of dialogism, this study aims to predict blogger OKBC response. Manipulating the blog topic (well-defined vs. ill-defined) and the visitor inquiry format (off-topic vs. on-topic), a field experiment with a 2 × 2 factorial design was conducted on a sample of N = 68 blogger communities with a total of 25, 303 members. For the entire sample, the community response was influenced only by the inquiry format. In a separate examination of the experimental groups, however, this remained true only for the well-defined topic, whereas for the ill-defined topic the community response only depended on the previously established dialog quality. The findings suggest identification criteria for responsive communities, which can support newcomer integration in OKBCs and, from a larger perspective, the use of OKBCs as components of formal learning environments. Highlights: We examined collaborative dialog in online knowledge building communities. Community response is an essential prerequisite of informal learning. Automated dialog analysis based on Bakhtin's dialogism was employed. For all and well-defined topics, only inquiry format influenced communityAbstract: Informal learning in online knowledge building communities (OKBCs) often starts with online academic help seeking, and with visitor inquiries on specific topics. In such a context, learning presupposes adequate OKBC response. Employing a social learning analytics approach based on natural language processing and Bakhtin's theory of dialogism, this study aims to predict blogger OKBC response. Manipulating the blog topic (well-defined vs. ill-defined) and the visitor inquiry format (off-topic vs. on-topic), a field experiment with a 2 × 2 factorial design was conducted on a sample of N = 68 blogger communities with a total of 25, 303 members. For the entire sample, the community response was influenced only by the inquiry format. In a separate examination of the experimental groups, however, this remained true only for the well-defined topic, whereas for the ill-defined topic the community response only depended on the previously established dialog quality. The findings suggest identification criteria for responsive communities, which can support newcomer integration in OKBCs and, from a larger perspective, the use of OKBCs as components of formal learning environments. Highlights: We examined collaborative dialog in online knowledge building communities. Community response is an essential prerequisite of informal learning. Automated dialog analysis based on Bakhtin's dialogism was employed. For all and well-defined topics, only inquiry format influenced community response. For ill-defined topics only dialog quality influenced community response. … (more)
- Is Part Of:
- Computers in human behavior. Volume 89(2018)
- Journal:
- Computers in human behavior
- Issue:
- Volume 89(2018)
- Issue Display:
- Volume 89, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 89
- Issue:
- 2018
- Issue Sort Value:
- 2018-0089-2018-0000
- Page Start:
- 349
- Page End:
- 354
- Publication Date:
- 2018-12
- Subjects:
- Online knowledge building communities -- Newcomer integration -- Social learning analytics -- Dialog analysis -- Academic help seeking
Interactive computer systems -- Periodicals
Man-machine systems -- Periodicals
004.019 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07475632 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.chb.2018.08.034 ↗
- Languages:
- English
- ISSNs:
- 0747-5632
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.921600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25775.xml