2022-2025: CoMaCl: Detecting overlapping clusters in Concept Maps respecting its Content

An important feature of successful learning is the networking of specialised knowledge, which, for example, is characterised in experts – who have learned very successfully in their domain – by highly networked cognitive structures. Networked knowledge enables, for example, a high quality of self-explanatory learning, because even if necessary specialised knowledge is provided to laypersons, this lexicon of knowledge does not replace well-networked knowledge.
Open concept mapping tasks are suitable for capturing the cognitive structure of a test person and these are already being used in various studies. Capturing the interconnectedness of subject knowledge is all the more relevant at present because the quality of individuals' assessments in complex contexts (such as climate change as a socioscientific issue) is related to the quality of cognitive structures. Complex, multidisciplinary teaching should lead to a high level of interconnectedness of knowledge from different domains. It is therefore desirable to be able to evaluate concept maps from open concept mapping tasks in a similarly economical way in order to assess the cognitive structures of test subjects and to gauge the success of learning by connecting subject knowledge. So far, however, there are no economical evaluation methods for concept maps from open concept mapping tasks that do not involve a significant loss of information.

This PhD project therefore aims to develop a new, economical and valid test procedure for networked knowledge. To achieve this, an evaluation algorithm is being developed that takes into account both the structural and semantic content of individual concept maps in order to identify overlapping clusters of concepts in these concept maps.
With the help of the clusters determined in this way, new information about the concept maps can be determined, such as: ‘the number of clusters’ or ‘bridge concepts that connect several clusters’. These and further information are examined to see what conclusions can be drawn about the quality of the concept maps and thus about the quality of the underlying cognitive structures.

 

Publications

Schuck, P. & Höttecke, D (i.V.). Kognitive Strukturen erheben in Studien mit großen Stichproben: Entwicklung eines Algorithmus. In H.v. Vorst (Hrsg.), Entdecken, lehren und forschen im Schülerlabor. Gesellschaft für Didaktik der Chemie und Physik. Jahrestagung in Bochum 2024.