SLOAN: Social Learning Optimization Analysis of Networks

The International Review of Research in Open and Distributed Learning Pub Date : 2022-11-01 DOI:10.19173/irrodl.v23i4.6484

D. Lemay, Tenzin Doleck, Christopher G. Brinton

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Abstract

Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning.

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斯隆:网络的社会学习优化分析

在线讨论研究主要采用案例法进行。本文提出了一种基于信息熵和全球网络效率等网络指标的比较分析方法，作为表征社会学习群体动态的更全面的措施。我们将社会学习优化网络分析(SLOAN)应用于由Coursera多个学科的课程组成的数据集。我们考察了论坛使用和网络效率测量的关系，其特征是通过网络的信息流。论坛的大小和用途各不相同。在大规模开放的在线课程讨论论坛中，主题相关话题和程序性话题更普遍的课程在寻求但不传播行为方面存在显著差异。与主题相关的谈话与更高的网络效率相关，总体上具有更高的寻找和传播得分。我们讨论了斯隆对社会学习的价值，并主张使用讨论帖子推荐系统进行在线讨论优化的实验研究，以最大化社会学习。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The International Review of Research in Open and Distributed Learning

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