Toward collaboration sensing
Toward collaboration sensing
复制标题
迈向协作感知
DOI：
10.1007/s11412-014-9202-y
复制
发表时间：
2014
影响因子：
4.3
通讯作者：
R. Pea
中科院分区：
文献类型：
--
作者：
Bertrand Schneider;R. Pea
我们描述了网络分析技术的初步应用，在协作学习活动中收集的眼动跟踪数据。本文有三个贡献：首先，我们将协作眼动跟踪数据可视化为网络，其中图的节点表示注视，边表示扫视。我们发现，这些表示可以作为出发点，制定研究问题和假设的协作过程。其次，可以计算网络指标来解释图的属性，并找到学生协作质量的代理。我们发现，我们的图的不同特征与学生合作的不同方面相关（例如，学生达成共识的程度与图的强连接组件的平均大小有关）。第三，我们使用这些特征来预测学生的合作质量，方法是将这些特征输入机器学习算法。我们发现，在我们考虑的八个合作维度中，我们能够大致预测（使用中位数分割）学生的合作质量，准确率在85%到100%之间。最后，我们讨论的影响，开发“协作传感”工具，并评论实施这种方法的正式学习环境。
We describe preliminary applications of network analysis techniques to eye-tracking data collected during a collaborative learning activity. This paper makes three contributions: first, we visualize collaborative eye-tracking data as networks, where the nodes of the graph represent fixations and edges represent saccades. We found that those representations can serve as starting points for formulating research questions and hypotheses about collaborative processes. Second, network metrics can be computed to interpret the properties of the graph and find proxies for the quality of students’ collaboration. We found that different characteristics of our graphs correlated with different aspects of students’ collaboration (for instance, the extent to which students reached consensus was associated with the average size of the strongly connected components of the graphs). Third, we used those characteristics to predict the quality of students’ collaboration by feeding those features into a machine-learning algorithm. We found that among the eight dimensions of collaboration that we considered, we were able to roughly predict (using a median-split) students’ quality of collaboration with an accuracy between ~85 and 100 %. We conclude by discussing implications for developing “collaboration-sensing” tools, and comment on implementing this approach for formal learning environments.