论文标题
检测：时间教育数据中行为趋势的分层聚类算法
DETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data
论文作者
论文摘要
聚集学生行为的技术通过提供学生学习的洞察力提供了许多机会来改善教育成果。但是，学生行为的一个重要方面，即随着时间的推移进化，使用现有方法通常可能具有挑战性。这是因为这些方法使用的目标函数并未明确旨在在时间上找到聚类趋势，因此结果可能无法清楚地表示这些趋势。本文介绍了“检测”（通过聚类时间序列数据引起的教育趋势的检测），这是一种新型的分裂分层聚类算法，将时间信息纳入其目标函数，以优先考虑对行为趋势的检测。所得的簇在结构上与决策树相似，其群集的层次结构是由功能的决策规则定义的。检测易于应用，高度可定制，适用于广泛的教育数据集，并产生易于解释的结果。通过对两种在线编程课程（n> 600）的案例研究，本文演示了两个示例的检测示例：1）确定队列行为如何随着时间的推移而发展和2）确定学生行为的表征，这些练习是许多学生放弃的练习。
Techniques for clustering student behaviour offer many opportunities to improve educational outcomes by providing insight into student learning. However, one important aspect of student behaviour, namely its evolution over time, can often be challenging to identify using existing methods. This is because the objective functions used by these methods do not explicitly aim to find cluster trends in time, so these trends may not be clearly represented in the results. This paper presents `DETECT' (Detection of Educational Trends Elicited by Clustering Time-series data), a novel divisive hierarchical clustering algorithm that incorporates temporal information into its objective function to prioritise the detection of behavioural trends. The resulting clusters are similar in structure to a decision tree, with a hierarchy of clusters defined by decision rules on features. DETECT is easy to apply, highly customisable, applicable to a wide range of educational datasets and yields easily interpretable results. Through a case study of two online programming courses (N>600), this paper demonstrates two example applications of DETECT: 1) to identify how cohort behaviour develops over time and 2) to identify student behaviours that characterise exercises where many students give up.
