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Examining Humans' Problem-Solving Styles in Technology-Rich Environments Using Log File Data
[journal article]
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... view more
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.... view less
Keywords
problem solving; experiential knowledge; learning; behavior
Classification
Social Psychology
Free Keywords
problem-solving style technology-rich environments; experiential learning theory; k-means clustering; explanatory item response modeling; log file data; PIAAC
Document language
English
Publication Year
2022
Page/Pages
p. 1-18
Journal
Journal of Intelligence, 10 (2022) 3
DOI
https://doi.org/10.3390/jintelligence10030038
ISSN
2079-3200
Status
Published Version; peer reviewed