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Social Motif Analytics: Network Building Blocks for Assessing Participation in an Online Engineering Community

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Conference

2014 ASEE Annual Conference & Exposition

Location

Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014

ISSN

2153-5965

Conference Session

Data Analytics in Education

Tagged Division

Computers in Education

Page Count

10

Page Numbers

24.1088.1 - 24.1088.10

DOI

10.18260/1-2--23021

Permanent URL

https://strategy.asee.org/23021

Download Count

179

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Paper Authors

author page

Hon Jie Teo Virginia Tech

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Abstract

Assessing Online Learning through the Knowledge Creation Framework: A Social Network and NounPhrase Analysis ApproachAbstractThe increased use of computers and greater accessibility of the internet have triggered numerouseducational innovations such as online discussion forums, wiki, Open Educational Resources, MOOCs, toname a few. These advances have brought with them a wide range of instructional videos, writtendocuments and discussion archives that provide opportunities for engineering learners to expand theirlearning beyond the engineering classroom. However, it remains a challenging task to assess the qualityof learning and participation on these learning platforms particularly due to the informal nature ofengagement as a whole and the massive amount of learner-produced data. The purpose of this study isto assess online learning by characterizing and describing the advancement of knowledge in an onlinecommunity for electrical and electronics engineering learners (AllAboutCircuits.com). The theoreticalperspective that informed this study was Knowledge Creation (Paavola & Hakkarainen, 2005) wherelearning is understood as a collaborative and discovery process of developing shared knowledgeartifacts for the collective benefit of participants in a community. According to this perspective, thequality of learning and participation on an online community can be in understood in terms of thedegree of collaboration in advancing knowledge artifacts and, whether online discussions are sustainedand collaborative. Data mining techniques are used to collect and organize half a million messages forclose to a hundred thousand pages of discussion and to extract user-produced data such as text andengineering drawings, and linkages between learners. The data is then analyzed by social networkanalysis and noun phrase analysis. Findings from this study suggest learners benefit from theparticipation and contributions of established members in discussions. Through social network analysis,it is observed that learners who receive the attention of established online members are more likely toparticipate in a more sustained discussion. From noun phrase analysis, the study found that onlinediscussion is often richer in content and language use when learners proactively reply to other onlinecommunity members and acknowledge their contributions. Overall, the study found evidence thatsuggests that interaction dynamics between online learners and established community members play acrucial role in the evaluation of the quality of learning on online discussion forums.

Teo, H. J. (2014, June), Social Motif Analytics: Network Building Blocks for Assessing Participation in an Online Engineering Community Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--23021

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