The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at understanding students’ learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students’ participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension.

中文翻译：

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通过社交网络分析捕获计算机支持的协作学习的参与度和社交维度：哪种方法和措施很重要？

在正式和非正式学习环境中，数字学习工具和平台的使用日益广泛，已为获取大量学习者数据提供了广泛的机会，对这些数据的分析旨在了解学生的学习过程，改善学习成果，提供学习者支持以及教学。目前，此类数据已从在线学习管理系统中的讨论论坛中大量访问，并且已通过应用社交网络分析（SNA）进行了进一步分析。但是，这些分析的结果并非总是可重复的。由于此类学习分析（LA）方法将评估作为流程的第一步，因此，用于衡量协作学习活动的所选技术的鲁棒性对于提高透明度至关重要，结果的可重复性和概括性。本文介绍了一项研究的发现，该研究的重点是在洛杉矶和国民账户体系研究领域中经常使用的关键集中性措施的验证。我们研究了不同的网络配置（即多图，加权和简化）如何影响集中度度量的可重复性和鲁棒性，这些度量是CSCL设置中学生学习的指标。特别是，这项研究旨在为提供健壮有效的方法，以衡量和更好地理解协作学习的参与度和社会维度。该研究是基于12所大学课程的数据集进行的。结果表明，多图配置可产生最一致且最可靠的中心度度量。研究结果还表明，用多图方法计算的学位中心是学生参与工作的可靠指标，也是他们表现的一致预测指标。同样，特征向量中心性是最可靠的中心性，可以可靠地表示社会维度，而与网络配置无关。这项研究为适当的网络表示形式提供了指导，并为如何可靠地为每个维度选择适当的指标提供了合理的建议。