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• W2017843312 abstract "In an experimental study, we investigated how 10th graders acquire, extend, and successively relate knowledge about qualitative and quantitative aspects of classical mechanics. The study was made up of 2 main phases. In the 1st phase, students were taught either qualitative or quantitative aspects of classical mechanics by means of 2 different instructional units. In both units, concept maps were used for the presentation of information. In the 2nd phase, dyads were formed with students who worked on different instructional units and thus possessed systematically different knowledge. The 2 students collaboratively worked on problems that were beyond the competence of each of them and demanded the coordinated useof knowledge about qualitative and quantitative aspects. Before and after the instruction as well as after the collaborative problem solving, students had to work on multicomponent tests. In addition, during the collaborative problem solving, protocols were taken of the students' mutual exchange of information An analysis of the multicomponent tests revealed that qualitative as well as quantitative knowledge can successfully be taught by means of concept maps. Students who initially were taught qualitative aspects of physics gained significantly more from the information provided by their quantitatively instructed partners during the collaborative problem solving than the other way around. An analysis of individual problem-solving attempts uncovered that students learned to construct increasingly more complete qualitative and quantitative problem representations. An explorative protocol analysis of the students' dialogues further indicated that the students gradually shifted their focus from quantitative to qualitative problem representations during collaborative problem solving." @default.
• W2017843312 created "2016-06-24" @default.
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• W2017843312 date "1999-04-01" @default.
• W2017843312 modified "2023-09-25" @default.
• W2017843312 title "Learning to Relate Qualitative and Quantitative Problem Representations in a Model-Based Setting for Collaborative Problem Solving" @default.
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• W2017843312 doi "https://doi.org/10.1207/s15327809jls0802_1" @default.
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