Statistical Analysis of Complex Problem-Solving Process Data: An Event History Analysis Approach

Frontiers in Psychology

article

Yunxiao Chen¹  Xiaoou Li²  Jingchen Liu³  Zhiliang Ying³ 

[1] Department of Statistics, London School of Economics and Political Science, United Kingdom;School of Statistics, University of Minnesota, United States;Department of Statistics, Columbia University, United States

关键词: process data;    complex problem solving;    PISA data;    response time;    event history analysis;   

DOI  :  10.3389/fpsyg.2019.00486

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Complex problem-solving (CPS) ability has been recognized as a central 21st century skill. Individuals' processes of solving crucial complex problems may contain substantial information about their CPS ability. In this paper, we consider the prediction of duration and final outcome (i.e., success/failure) of solving a complex problem during task completion process, by making use of process data recorded in computer log files. Solving this problem may help answer questions like “how much information about an individual's CPS ability is contained in the process data?,” “what CPS patterns will yield a higher chance of success?,” and “what CPS patterns predict the remaining time for task completion?” We propose an event history analysis model for this prediction problem. The trained prediction model may provide us a better understanding of individuals' problem-solving patterns, which may eventually lead to a good design of automated interventions (e.g., providing hints) for the training of CPS ability. A real data example from the 2012 Programme for International Student Assessment (PISA) is provided for illustration.

【 授权许可】

CC BY