Original Research ARTICLE
Mapping Background Variables with Sequential Patterns in Problem-Solving Environments: An Investigation of U.S. Adults’ Employment Status in PIAAC
- 1University of Maryland, College Park, United States
- 2Educational Testing Service, United States
Adult assessments have evolved to keep pace with the changing nature of adult literacy and learning demands. As the importance of information and communication technologies (ICT) continues to grow, measures of ICT literacy skills, digital reading, and problem-solving in technology-rich environments (PSTRE) are increasingly important topics for exploration through computer-based assessment (CBA). This study used process data collected in log files from the Programme for the International Assessment of Adult Competencies (PIAAC) and survey data from this assessment, with a focus on the U.S. sample, to (a) identify employment-related background variables that significantly related to PSTRE skills and problem-solving behaviors, and (b) extract robust sequences of actions by subgroups categorized by significant variables. We conducted this study in two phases. First, we used regression analyses to select background variables that significantly predict the general PSTRE, literacy, and numeracy skills, as well as the response time and correctness in the example item. Second, we identified typical action sequences by different subgroups using the chi-square feature selection model to explore these sequences and differentiate the subgroups. Based on the malleable factors associated with problem-solving skills, the goal of this study is to provide information for improving competences in adult education for targeted groups.
Keywords: Process data, Problem Solving, sequential pattern, Background variables, large-scale assessment, PIAAC
Received: 23 Aug 2018;
Accepted: 07 Mar 2019.
Edited by:Holmes Finch, Ball State University, United States
Reviewed by:Hongyun Liu, Beijing Normal University, China
Daniel Bolt, University of Wisconsin-Madison, United States
Copyright: © 2019 Liao, He and Jiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Dandan Liao, University of Maryland, College Park, College Park, 20742, Maryland, United States, email@example.com