Concept Mapping in Magnetism and Electrostatics

Core Concepts and Development over Time

Datos Bibliográficos

ID22048005
AutoresChristian M Thurn (0000-0002-5942-3273, ETH Zurich, autor de correspondencia), Brigitte Hänger (Professur für Naturwissenschaftsdidaktik, FHNW, Hofackerstrasse 30, 4132 Muttenz, Switzerland), Tommi Kokkonen (0000-0003-3324-2690, University of Helsinki)
Año2020
Volumen10
Número5
Páginas129
Fecha de publicación2020-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEducation Sciences (JOURNAL)
Identificadores de la revistaISSN: 2227-7102 • E-ISSN: 2227-7102
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci10050129
OpenAlexW3018630162
IdiomaEN
Citas recibidas4
Referencias citadas43

Conceptual change theories assume that knowledge structures grow during the learning process but also get reorganized. Yet, this reorganization process itself is hard to examine. By using concept maps, we examined the changes in students’ knowledge structures and linked it to conceptual change theory. In a longitudinal study, thirty high-achieving students (M = 14.41 years) drew concept maps at three timepoints across a teaching unit on magnetism and electrostatics. In total, 87 concept maps were analyzed using betweenness and PageRank centrality as well as a clustering algorithm. We also compared the students’ concept maps to four expert maps on the topic. Besides a growth of the knowledge network, the results indicated a reorganization, with first a fragmentation during the unit, followed by an integration of knowledge at the end of the unit. Thus, our analysis revealed that the process of conceptual change on this topic was non-linear. Moreover, the terms used in the concept maps varied in their centrality, with more abstract terms being more central and thus more important for the structure of the map. We also suggest ideas for the usage of concept maps in class

Betweenness centrality · Centrality · Concept learning · Concept map · Machine learning · Multidimensional scaling · PageRank · Advanced Text Analysis Techniques · Computer Science · Innovative Teaching and Learning Methods · Mathematics · Science Education and Pedagogy · Artificial Intelligence · Theoretical Computer Science