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The role of social network analysis as a learning analytics tool in online problem based learning

机译：社交网络分析在基于问题的在线学习中作为学习分析工具的作用

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Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students’ positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance. The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students’ position and interaction parameters are associated with better performance. This study involved 135 students and 15 teachers in 15 PBL groups in the course of “growth and development” at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants’ roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test. The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students’ level of activity (outdegree rs(133)?=?0.27, p?=?0.01), interaction with tutors (rs (133)?=?0.22, p?=?0.02) are positively correlated with academic performance. Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students’ activity.
机译：社交网络分析（SNA）在整个技术增强学习和特别是在线（基于问题的学习）PBL中的交互研究中可能具有未开发的价值。使用SNA来研究学生在信息交换网络，交流活动和互动中的位置，我们可以加深对PBL过程的理解，评估每个参与者角色的重要性，并了解互动如何影响学习成绩。这项研究的目的是研究如何使用SNA视觉和数学分析来调查在线PBL，此外，还可以查看学生的位置和互动参数是否与更好的表现相关。这项研究在Qassim大学的“成长与发展”过程中，涉及15个PBL组的135名学生和15名教师。本课程使用混合式PBL作为教学方法。所有互动数据均从学习管理系统中提取，并使用SNA视觉和数学技术在单个学生和小组级别进行了分析，计算了中心性度量，并绘制了参与者的角色。使用非参数Spearman秩相关检验进行变量之间的相关。该课程有2620个在线互动，主要是学生与学生的互动（占89％），学生与教师的互动是4.9％，教师与学生的互动是6.15％。结果表明，SNA视觉分析可以精确地绘制每个PBL组和组内活动的水平，并概述组参与者之间的互动，确定孤立和活跃的学生（领导者和促进者）并评估导师的角色。统计分析表明，学生的活动水平（rs（133）≥0.27，p = 0.01），与教师互动（rs（133）≥0.22，p = 0.02）呈正相关。具有学术表现。社交网络分析是一种实用的方法，可以可靠地监视在线PBL环境中的交互。使用SNA可以揭示有关课程，小组和个别学生的重要信息。 SNA生成的见解在学习分析的背景下可能有用，有助于监视学生的活动。

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