ATP-OC
Adaptive tutored problem-solving in organic chemistry

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In this project, the effectiveness of theory-based support in dealing with external representations in complex problem solving processes will be investigated using the example of reaction mechanisms in organic chemistry.

Project data


Research linesResearch Line Domain-Specific Learning in Kindergarten and School
DepartmentsChemistry Education, IPN Leibniz Institute for Science and Mathematics Education
FundingDeutsche Forschungsgemeinschaft
Period5/1/20234/30/2026
Statuscurrent
IPN researchersPD Dr. habil. Sascha Bernholt (Project lead), Anja Annemüller, Gyde Asmussen

Dealing with external representations is a central challenge for learners, especially in the natural sciences. For example, formulas and reaction mechanisms play a prominent role in chemistry. In the present project, three studies will be carried out to investigate the effectiveness of theory-based instructions in dealing with external representations in complex problem-solving processes using the example of reaction mechanisms in organic chemistry. The selected instructions focus on specific problem areas in dealing with reaction mechanisms known form the literature. When learning with solved examples, the explicit representation of the problem-solving procedure intends to reduce the students’ cognitive load and to support them in developing their own problem-solving strategies. In addition, special potential is seen in the integration of perceptual learning, as this form of learning aims at automating pattern recognition and classification processes and can thus support the recall and application of already existing declarative knowledge. In combination with these two forms of learning, it will also be investigated to what extent adaptive, individualized scaffolds, integrated in the problem-solving process, can further support students' problem-solving. In addition to the development and evaluation of specific instructional materials, a better understanding of how students can be better supported in complex problem-solving processes will be created.