Department of Computer and System Sciences (DSV), Stockholm University, Kista, Stockholm, Sweden.
Qassim University, College of Medicine, Qassim, Melida, Kingdom of Saudi Arabia.
PLoS One. 2018 Mar 22;13(3):e0194777. doi: 10.1371/journal.pone.0194777. eCollection 2018.
To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.
为确保在线协作学习达到预期的教学目标(即真正具有协作性并能激发学习),需要有机制来监测在线协作的效率。各种研究表明,社会网络分析特别适用于研究学生在在线协作中的互动。然而,教育领域的研究仅关注使用 SNA 的理论潜力,而没有关注它们实际取得的收益。本研究探讨了如何使用社会网络分析来监测在线协作学习、发现需要改进的方面、指导有针对性的干预,以及在三个为期一整个学期的课程中使用实验性、观察性重复测量设计来评估干预的效果。本研究采用基于 SNA 的可视化和定量分析相结合的方法,为每个参与者监测了三个 SNA 结构:互动水平、信息交换中的角色和位置,以及每个参与者在协作中的角色。在小组层面上,我们监测了互动性和小组凝聚力指标。我们的监测揭示了在三个研究课程中存在一种非协作性的以教师为中心的互动模式,以及学生之间很少互动、信息交流或协商有限,以及由教师主导的非常有限的学生网络。在此基础上设计了一项基于 SNA 洞察的干预措施。该干预措施分为五个步骤:提高意识、促进协作、改进内容、培训教师,最后通过反馈进行练习。对干预措施的评估表明,它显著增强了学生之间和师生之间的互动,大多数学生和教师之间产生了协作性的互动模式。由于高效和富有成效的活动是成功进行内容讨论和实现协作目标的必要前提,我们建议我们基于 SNA 的方法将积极影响许多教育领域的教学和学习。我们的研究为 SNA 可以为监测和支持高等教育中的教学和学习提供什么提供了概念验证。