Using Social Network Analysis to Measure the Effect of Learning Analytics in Computing Education

2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) Pub Date : 2019-07-01 DOI:10.1109/ICALT.2019.00044

Daniel M. Olivares, R. F. Mello, Olusola O. Adesope, V. Rolim, D. Gašević, C. Hundhausen

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引用次数: 7

Abstract

Student retention and learning in STEM disciplines is a growing problem. The 2012 report by the US President's Council of Advisors on Science and Technology (PCAST) predicts a future deficit in science, engineering, and mathematics (STEM) in the following decade and emphasizes the importance of addressing this issue. With this as a motivating factor, the OSBLE+ Social Programming Environment (SPE) was used to leverage social and programming data for the basis of automatically generated prompts inserted into the SPE. These prompts were designed to stimulate help-seeking, help-giving, and social interaction in the learning environment. A social network analysis was performed in order to determine whether exposure to the automated interventions would positively affect the relationship among students over time. Results of this study suggest that students in the experimental treatment who were presented with automated prompts developed more connected and social networks than those in the control treatment.

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使用社会网络分析来衡量学习分析在计算机教育中的效果

学生在STEM学科中的保留和学习是一个日益严重的问题。美国总统科学技术顾问委员会(PCAST) 2012年的报告预测，未来十年，科学、工程和数学(STEM)领域将出现赤字，并强调了解决这一问题的重要性。有了这个激励因素，我们使用OSBLE+ Social Programming Environment (SPE)来利用社交和编程数据，将自动生成的提示插入到SPE中。这些提示被设计用来刺激学生在学习环境中寻求帮助、给予帮助和社会互动。一项社会网络分析是为了确定随着时间的推移，接触自动化干预是否会对学生之间的关系产生积极影响。本研究的结果表明，实验组的学生在接受自动提示的情况下，比对照组的学生建立了更多的联系和社交网络。

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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2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)

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