基于计算机的评估提供了收集与解决问题过程相关的行为数据的新来源的机会,称为日志文件数据。为了理解可以从这些过程数据中发现的行为模式,许多研究都采用了聚类方法。与单模式聚类算法相比,这项研究利用了双聚类方法,允许同时对测试者和从日志文件中提取的特征进行分类。通过将双聚类算法应用于PISA2012CPS评估中的“票证”任务,我们评估了双聚类算法在从过程数据中识别和解释同质双聚类方面的潜力.与单模式聚类算法相比,双聚类方法可以发现在特征变量子集上同质的个体集群,持有承诺,获得对学生解决问题的行为模式的细粒度见解。实证结果表明,特定的特征子集在识别双色花序中起着至关重要的作用。此外,这项研究探索了双聚类在动作序列数据和时序数据上的利用,以及基于时间的特征的纳入增强了学生对分析背景下的动作序列和分数的理解。
Computer-based assessments provide the opportunity to collect a new source of behavioral data related to the problem-solving process, known as log file data. To understand the behavioral patterns that can be uncovered from these process data, many studies have employed clustering methods. In contrast to one-mode clustering algorithms, this study utilized biclustering methods, enabling simultaneous classification of test takers and features extracted from log files. By applying the biclustering algorithms to the \"Ticket\" task in the PISA 2012 CPS assessment, we evaluated the potential of biclustering algorithms in identifying and interpreting homogeneous biclusters from the process data. Compared with one-mode clustering algorithms, the biclustering methods could uncover clusters of individuals who are homogeneous on a subset of feature variables, holding promise for gaining fine-grained insights into students\' problem-solving behavior patterns. Empirical results revealed that specific subsets of features played a crucial role in identifying biclusters. Additionally, the study explored the utilization of biclustering on both the action sequence data and timing data, and the inclusion of time-based features enhanced the understanding of students\' action sequences and scores in the context of the analysis.