Design of a computational model for social learning support and analystics

Neil Y. Yen, Jason C. Hung, Chia Chen Chen*, Qun Jin

*この研究の対応する著者

研究成果: Article査読

3 被引用数 (Scopus)

抄録

Conventional online learning typically allows an instructor to deliver instruction to students via a predefined curriculum and within a fixed knowledge structure (i.e., explaining the instructional subject). With the dramatic growth of social media technology and correlated data aggregation, some sort of instant knowledge is obtained by daily users. An emerging type of knowledge (i.e., social knowledge) has been identified and may lead to self-paced learning from social networks, which is simply defined as social learning. This article points out three important issues for social learning, namely, knowledge retrieval via temporal social factors, and the connection between social network and the knowledge domain. Two significant automation mechanisms, lecture generation for self-regulated learning and influencing domain computation for opportunity finding, are suggested to facilitate the process of social learning. A prototype system based on Elgg was implemented, sourced by a federated repository that has stored and shared more than 1.5 millions transactions (e.g., content, interactions, etc.). We conclude that timely social knowledge (or crowdsourcing results) can be widely applied in the next era of online learning environment. Findings through the statistical analysis are prospective to support understanding of phenomenon of social learning and design of future learning platform for followup researchers.

本文言語English
ページ(範囲)547-561
ページ数15
ジャーナルComputers in Human Behavior
92
DOI
出版ステータスPublished - 2019 3

ASJC Scopus subject areas

  • 人文科学(その他)
  • 人間とコンピュータの相互作用
  • 心理学(全般)

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