Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Journal of Intelligence |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3200/10/3/38 |
| _version_ | 1797486418052775936 |
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| author | Yizhu Gao Xiaoming Zhai Okan Bulut Ying Cui Xiaojian Sun |
| author_facet | Yizhu Gao Xiaoming Zhai Okan Bulut Ying Cui Xiaojian Sun |
| author_sort | Yizhu Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:33:56Z |
| format | Article |
| id | doaj.art-319ec4276c5d4c9897f38532af16ad2a |
| institution | Directory Open Access Journal |
| issn | 2079-3200 |
| language | English |
| last_indexed | 2024-03-09T23:33:56Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Intelligence |
| spelling | doaj.art-319ec4276c5d4c9897f38532af16ad2a2023-11-23T17:04:53ZengMDPI AGJournal of Intelligence2079-32002022-06-011033810.3390/jintelligence10030038Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File DataYizhu Gao0Xiaoming Zhai1Okan Bulut2Ying Cui3Xiaojian Sun4Department of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, CanadaDepartment of Mathematics, Science, and Social Studies Education, University of Georgia, Athens, GA 30602, USADepartment of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, CanadaDepartment of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, CanadaSchool of Mathematics and Statistics, Southwest University, Chongqing 400715, ChinaThis 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.https://www.mdpi.com/2079-3200/10/3/38problem-solving style technology-rich environmentsexperiential learning theory<i>k</i>-means clusteringexplanatory item response modelinglog file data |
| spellingShingle | Yizhu Gao Xiaoming Zhai Okan Bulut Ying Cui Xiaojian Sun Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data Journal of Intelligence problem-solving style technology-rich environments experiential learning theory <i>k</i>-means clustering explanatory item response modeling log file data |
| title | Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data |
| title_full | Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data |
| title_fullStr | Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data |
| title_full_unstemmed | Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data |
| title_short | Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data |
| title_sort | examining humans problem solving styles in technology rich environments using log file data |
| topic | problem-solving style technology-rich environments experiential learning theory <i>k</i>-means clustering explanatory item response modeling log file data |
| url | https://www.mdpi.com/2079-3200/10/3/38 |
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