Summary: | 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: <i>Acting</i> indicates a preference for active explorations; <i>Reflecting</i> represents a tendency to observe; and <i>Shirking</i> shows an inclination toward scarce tryouts and few observations. Explanatory item response modeling analyses disclosed that individuals with the <i>Acting</i> style outperformed those with the <i>Reflecting</i> or the <i>Shirking</i> style, and this superiority persisted across tasks with different difficulties.
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