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. 2022 Jun 30;10(3):38.
doi: 10.3390/jintelligence10030038.

Examining Humans' Problem-Solving Styles in Technology-Rich Environments Using Log File Data

Yizhu Gao  1 , Xiaoming Zhai  2 , Okan Bulut  1 , Ying Cui  1 , Xiaojian Sun  3
Affiliations

Examining Humans' Problem-Solving Styles in Technology-Rich Environments Using Log File Data

Yizhu Gao et al. J Intell. .

Abstract

This study investigated how one's problem-solving style impacts his/her problem-solving performance in technology-rich environments. Drawing upon experiential learning theory, we extracted two behavioral indicators (i.e., planning duration for problem solving and human-computer interaction frequency) to model problem-solving styles in technology-rich environments. We employed an existing data set in which 7516 participants responded to 14 technology-based tasks of the Programme for the International Assessment of Adult Competencies (PIAAC) 2012. Clustering analyses revealed three problem-solving styles: Acting indicates a preference for active explorations; Reflecting represents a tendency to observe; and Shirking shows an inclination toward scarce tryouts and few observations. Explanatory item response modeling analyses disclosed that individuals with the Acting style outperformed those with the Reflecting or the Shirking style, and this superiority persisted across tasks with different difficulties.

Keywords: experiential learning theory; explanatory item response modeling; k-means clustering; log file data; problem-solving style technology-rich environments.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
This is an exemplary problem-solving item in TRE. From Job Search Part I, by (OECD n.d.) (https://piaac-logdata.tba-hosting.de/public/problemsolving/JobSearchPart1/pages/jsp1-home.html) (accessed on 11 August 2021).
Figure 2
Figure 2
Examples of how polytomous and dichotomous responses are defined as pseudo-dichotomous responses.
Figure 3
Figure 3
The optimal number of clusters by the average silhouette method for the two behavioral indicators.
Figure 4
Figure 4
The optimal number of clusters suggested by the majority rule of the NbClust package for the two behavioral indicators.
Figure 5
Figure 5
Behavioral profiles of the three clusters on the two behavioral indicators.
See this image and copyright information in PMC

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