Developing Undergraduate Students' Collaborative Problem Solving Ability through Data-supported Reflection

In the context of rapid development of knowledge economy, Collaborative Problem Solving (CPS) ability has become an important concern in the field of education, which not only embodies the ability of individuals to solve problems independently, but also emphasizes the importance of effective communication and cooperation among team members to deal with complex tasks. For college students, CPS is a key component of their comprehensive literacy, which helps them adapt to the challenges of the future workplace. This study designed and tested an instructional model incorporating data-supported reflection with the aim of exploring the model's impact on college students' CPS behaviors and their development of viewpoint complexity and metacognition. Data-supported reflection provided students with objective feedback to help them accurately assess their own and their team's performance, thereby optimizing learning strategies and improving self-regulation and metacognition. The results indicated that this instructional model significantly enhanced students' CPS behaviors and promoted growth in viewpoint complexity and metacognitive development. Through data-supported reflection, students were able to more effectively engage in collaborative problem solving in a team environment and promote depth and breadth of knowledge building. This not only enhanced individual learning, but also improved the overall effectiveness of the team, providing a solid foundation for developing college students with higher-order thinking skills and innovation.

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Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1109/cste64638.2025.11092005
Primary Topic
Innovative Teaching and Learning Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Developing Undergraduate Students' Collaborative Problem Solving Ability through Data-supported Reflection

Yuqin Yang, Chenxi Huang, Yu‐Ting Huang, Qianqian Ma et al.
Innovative Teaching and Learning Methods
article

Developing Undergraduate Students' Collaborative Problem Solving Ability through Data-supported Reflection

Yuqin Yang, Chenxi Huang, Yu‐Ting Huang, Qianqian Ma, Xinyue Zhao, Yin Shao
article en

Abstract

In the context of rapid development of knowledge economy, Collaborative Problem Solving (CPS) ability has become an important concern in the field of education, which not only embodies the ability of individuals to solve problems independently, but also emphasizes the importance of effective communication and cooperation among team members to deal with complex tasks. For college students, CPS is a key component of their comprehensive literacy, which helps them adapt to the challenges of the future workplace. This study designed and tested an instructional model incorporating data-supported reflection with the aim of exploring the model's impact on college students' CPS behaviors and their development of viewpoint complexity and metacognition. Data-supported reflection provided students with objective feedback to help them accurately assess their own and their team's performance, thereby optimizing learning strategies and improving self-regulation and metacognition. The results indicated that this instructional model significantly enhanced students' CPS behaviors and promoted growth in viewpoint complexity and metacognitive development. Through data-supported reflection, students were able to more effectively engage in collaborative problem solving in a team environment and promote depth and breadth of knowledge building. This not only enhanced individual learning, but also improved the overall effectiveness of the team, providing a solid foundation for developing college students with higher-order thinking skills and innovation.

Central China Normal University (CN)
Central China Normal University
Openalex Percentile: Top 78%
Innovative Teaching and Learning Methods
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Developing Undergraduate Students' Collaborative Problem Solving Ability through Data-supported Reflection — Yuqin Yang, Chenxi Huang, et al. · (2025) | TGRS Research Map | TGRS