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Networks identify productive forum discussions

机译:网络识别生产性论坛讨论

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摘要

Discussion forums provide a channel for students to engage with peers and course material outside of class, accessible even to commuter and nontraditional populations. Forums can build classroom community and aid learning, but students do not always take up these tools. We use network analysis to compare three semesters of forum logs from an introductory calculus-based physics course. The networks show dense structures of collaboration that differ significantly between semesters, even though aggregate participation statistics remain steady. After characterizing network structure for each semester, we correlate students’ centrality—a numeric measure of network position—with final course grade. Finally, we use a backbone extraction procedure to clean up “noise” in the network and clarify centrality-grade correlations. We find that more central network positions are positively linked with course success in the two semesters with denser forum networks. Centrality is a more reliable indicator of grade than non-network measures such as postcount. Backbone extraction destroys these correlations, suggesting that the noise is in fact signal and further analysis of the discussion transcripts is required.
机译:讨论论坛为学生提供渠道,以便在课外与同行和课程材料进行互动,甚至可以访问通勤者和非传统人口。论坛可以建立课堂社区和援助学习,但学生并不总是占用这些工具。我们使用网络分析从介绍性微积分的物理课程比较了三个论坛日志。网络显示了浓密的协作结构,这些结构在学期之间有显着差异,即使总参与统计数据仍然稳定。在每个学期的网络结构表征网络结构之后,我们将学生的中心性 - 一个网络位置的数字测量与最终课程等级相关联。最后,我们使用骨干提取过程来清理网络中的“噪声”并阐明中心等级相关性。我们发现更多的中央网络位置与具有更密集的论坛网络的两个学期内的课程成功与课程成功相连。中心性是比未网络措施更可靠的等级指标,如PostCount。骨干提取破坏了这些相关性,表明噪声实际上是信号,并且需要进一步分析讨论转录物。

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