Xu Haochen, Fang Guanhua, Chen Yunxiao, Liu Jingchen, Ying Zhiliang
Fudan University, Shanghai, China.
Columbia University, New York, NY, USA.
Appl Psychol Meas. 2018 Sep;42(6):478-498. doi: 10.1177/0146621617748325. Epub 2018 Apr 9.
Computer-based assessment of complex problem-solving abilities is becoming more and more popular. In such an assessment, the entire problem-solving process of an examinee is recorded, providing detailed information about the individual, such as behavioral patterns, speed, and learning trajectory. The problem-solving processes are recorded in a computer log file which is a time-stamped documentation of events related to task completion. As opposed to cross-sectional response data from traditional tests, process data in log files are massive and irregularly structured, calling for effective exploratory data analysis methods. Motivated by a specific complex problem-solving item "Climate Control" in the 2012 Programme for International Student Assessment, the authors propose a latent class analysis approach to analyzing the events occurred in the problem-solving processes. The exploratory latent class analysis yields meaningful latent classes. Simulation studies are conducted to evaluate the proposed approach.
基于计算机的复杂问题解决能力评估正变得越来越流行。在这样的评估中,考生的整个问题解决过程都会被记录下来,提供有关个人的详细信息,如行为模式、速度和学习轨迹。问题解决过程记录在计算机日志文件中,该文件是与任务完成相关事件的时间戳文档。与传统测试的横断面响应数据不同,日志文件中的过程数据量大且结构不规则,需要有效的探索性数据分析方法。受2012年国际学生评估项目中一个特定的复杂问题解决项目“气候控制”的启发,作者提出了一种潜在类别分析方法来分析问题解决过程中发生的事件。探索性潜在类别分析产生了有意义的潜在类别。进行了模拟研究以评估所提出的方法。