Statistical analysis of complex problem-solving process data: an event history analysis approach

Yunxiao Chen, Xiaoou Li, Jingchen Liu and Zhiliang Ying

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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.

Keywords: Complex problem solving; Event history analysis; PISA data; Process data; Response time (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2019-03-18
New Economics Papers: this item is included in nep-ore
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published in Frontiers in Psychology, 18, March, 2019, 10. ISSN: 1664-1078

Downloads: (external link)
http://eprints.lse.ac.uk/100871/ Open access version. (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:100871

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().