Gao Yizhu, Zhai Xiaoming, Bulut Okan, Cui Ying, Sun Xiaojian
Department of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, Canada.
Department of Mathematics, Science, and Social Studies Education, University of Georgia, Athens, GA 30602, USA.
J Intell. 2022 Jun 30;10(3):38. doi: 10.3390/jintelligence10030038.
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: indicates a preference for active explorations; represents a tendency to observe; and shows an inclination toward scarce tryouts and few observations. Explanatory item response modeling analyses disclosed that individuals with the style outperformed those with the or the style, and this superiority persisted across tasks with different difficulties.
本研究调查了一个人的问题解决风格如何影响其在技术丰富环境中的问题解决表现。借鉴体验式学习理论，我们提取了两个行为指标（即解决问题的规划时长和人机交互频率）来构建技术丰富环境中的问题解决风格模型。我们使用了一个现有数据集，其中7516名参与者对2012年成人能力国际评估项目（PIAAC）的14项基于技术的任务做出了回应。聚类分析揭示了三种问题解决风格：表明倾向于积极探索；代表观察的倾向；显示出尝试次数少且观察次数少的倾向。解释性项目反应建模分析表明，具有风格的个体比具有或风格的个体表现更好，并且这种优势在不同难度的任务中都持续存在。
