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Promoting young students' knowledge co-construction: a concept mapping-based online cooperative problem-posing approach.

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Title: Promoting young students' knowledge co-construction: a concept mapping-based online cooperative problem-posing approach.
Authors: Hwang, Gwo-Jen (AUTHOR), Chang, Chun-Chun (AUTHOR), Juan, Chin-Ya (AUTHOR)
Source: Educational Technology Research & Development. Jun2025, Vol. 73 Issue 3, p1741-1766. 26p.
Subjects: Concept mapping, Collaborative learning, Online education, Junior high schools, Problem solving, Group work in education, Inquiry method (Teaching), Student engagement
Abstract: Problem-posing has been regarded as a potential method to engage students in deeper thinking; however, without sufficient supports, most students could encounter difficulties in posing quality problems. In this study, a concept mapping-based online cooperative problem-posing (CM-OCPP) approach is proposed to guide students to complete problem-posing tasks in online collaborative learning contexts. To verify the effectiveness of the proposed approach, a quasi-experiment was conducted in a natural science class at a junior high school with two classes of seventh graders. Among them, one class with 29 students was assigned as the experimental group conducting the CM-OCPP approach; the other class with 29 students was the control group conducting the conventional online cooperative problem-posing (C-OCPP) approach. The research findings showed that the CM-OCPP group had better learning achievement, problem-solving skills, and problem-posing quality in terms of topic relevance and depth of questions than the C-OCPP group. The interactive pattern analysis results further showed that, with the guidance of the CM-OCPP approach, the students engaged more in knowledge sharing, discussion, knowledge co-construction, and diverse thinking. [ABSTRACT FROM AUTHOR]
Copyright of Educational Technology Research & Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Problem-posing has been regarded as a potential method to engage students in deeper thinking; however, without sufficient supports, most students could encounter difficulties in posing quality problems. In this study, a concept mapping-based online cooperative problem-posing (CM-OCPP) approach is proposed to guide students to complete problem-posing tasks in online collaborative learning contexts. To verify the effectiveness of the proposed approach, a quasi-experiment was conducted in a natural science class at a junior high school with two classes of seventh graders. Among them, one class with 29 students was assigned as the experimental group conducting the CM-OCPP approach; the other class with 29 students was the control group conducting the conventional online cooperative problem-posing (C-OCPP) approach. The research findings showed that the CM-OCPP group had better learning achievement, problem-solving skills, and problem-posing quality in terms of topic relevance and depth of questions than the C-OCPP group. The interactive pattern analysis results further showed that, with the guidance of the CM-OCPP approach, the students engaged more in knowledge sharing, discussion, knowledge co-construction, and diverse thinking. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Educational Technology Research & Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2025
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