[论文解读] Engagement, integration, involvement: supporting academic performance and developing a classroom social network

Eric A. Williams, Justyna P. Zwolak+2 more|arXiv (Cornell University)|Jun 13, 2017

Innovative Teaching and Learning Methods被引用 7

一句话总结

本研究运用网络分析方法，探究协作式物理课堂中学生社会互动如何预测学业表现。研究发现，学生在网络中的中心性，尤其是接近中心性，能显著预测未来成绩，且该效应在学期后半段显现，证实了课堂社会网络具有实质性发展过程，并对学习起到支持作用。

ABSTRACT

Theories developed by Tinto and Nora identify academic performance, learning gains, and involvement in learning communities as important facets of student engagement that support student persistence. Collaborative learning environments, such as those employed in the Modeling Instruction introductory physics course, are considered especially important because they provide students with the academic and social support required for success. Due to the inherently social nature of collaborative learning, we examined student social interactions in the classroom using Network Analysis. We used student centrality, a family of measures that quantify how connected or central a particular student is within the classroom network, to measure student engagement longitudinally over multiple times during the semester. Bootstrapped linear regression modeling showed that student centrality predicted future academic performance over and above prior GPA for five out of the six centrality measures tested; in particular, closeness centrality explained 29% more of the variance than prior GPA alone. These results confirm that student engagement in the classroom is critical to supporting academic performance. Furthermore, we found that this relationship emerged from social interactions that took place in the second half of the semester, suggesting the classroom network developed over time in a meaningful way.

研究动机与目标

• 探究学生在协作式学习环境中通过社会互动参与的程度如何影响学业表现。
• 考察课堂社会网络的纵向发展及其与学习成果的关系。
• 确定学生在课堂网络中的中心性指标是否能超越先前GPA，预测未来学业表现。
• 探究网络中心性与学业表现之间的关系在学期中何时显现。

提出的方法

• 应用网络分析方法，绘制建模教学物理课程中学生社会互动的网络图谱。
• 计算六种中心性指标（如度中心性、接近中心性、介数中心性）以量化学生在课堂网络中的连接程度。
• 使用自助抽样线性回归模型，检验中心性指标是否能预测未来学业表现，同时控制先前GPA的影响。
• 在学期内多个时间点收集互动数据，以评估网络结构的纵向变化。
• 聚焦于学期后半段进行分析，以确定预测关系何时显现。

实验结果

研究问题

• RQ1 学生在课堂社会网络中的中心性在多大程度上能超越先前GPA，预测未来学业表现？
• RQ2 网络中心性与学业表现之间的关系在一整个学期中如何演变？
• RQ3 在协作式学习环境中，哪种中心性指标最能预测学业表现？
• RQ4 网络中心性对学业表现的预测力在学期的哪个阶段变得显著？

主要发现

• 在自助抽样线性回归模型中，六种中心性指标中的五种显著预测了超越先前GPA的未来学业表现。
• 接近中心性解释的未来学业表现方差比仅凭先前GPA多出29%。
• 中心性与学业表现之间的关系在学期后半段才显现，表明课堂网络的形成具有发展性过程。
• 通过社会互动实现的学生参与度，特别是网络中的中心性，是学业成功的重要纵向预测指标。
• 研究结果支持协作式学习环境在促进社会融合与学业成就方面的作用。
• 本研究证实，课堂社会网络并非静态，而是以与学习成果相关的方式逐步演化。