Abstract
Concept maps, which are network-like visualisations of the inter-linkages between concepts, are used in teaching and learning as representations of students’ understanding of conceptual knowledge and its relational structure. In science education, research on the uses of concept maps has focused much attention on finding methods to identify key concepts that are of the most importance either in supporting or being supported by other concepts in the network. Here we propose a method based on network analysis to examine students’ representations of the relational structure of physics concepts in the form of concept maps. We suggest how the key concepts and their epistemic support can be identified through focusing on the pathways along which the information is passed from one node to another. Towards this end, concept maps are analysed as directed and weighted networks, where nodes are concepts and links represent different types of connections between concepts, and where each link is assumed to provide epistemic support to the node it is connected to. The notion of key concept can then be operationalised through the directed flow of information from one node to another in terms of communicability between the nodes, separately for out-going and in-coming weighted links. Here we analyse a collated concept network based on a sample of 12 original concept maps constructed by university students. We show that communicability is a simple and reliable way to identify the key concepts and examine their epistemic justification within the collated network. The communicabilities of the key nodes in the collated network are compared with communicabilities averaged over the set of 12 individual concept maps. The comparison shows the collated network contains an extensive set of key concepts with good epistemic support. Every individual networks contain a sub-set of these key concepts but with a limited overlap of the sub-sets with other individual networks. The epistemically well substantiated knowledge is thus sparsely distributed over the 12 individual networks.
Highlights
Learning scientific knowledge requires learning its key concepts and their lexicon: how these concepts are related, how they can be used with other concepts and how they are connected as part of a system of other concepts
The collated concept network consisting of 12 individual concept maps is shown in Fig. 2, where node sizes are scaled according to values of the out- and in-communicabilities GOUT (v) and GIN (v)
We have focused on finding the key concepts in how university students conceive the relational conceptual structure of electricity and magnetism
Summary
Learning scientific knowledge requires learning its key concepts and their lexicon: how these concepts are related, how they can be used with other concepts and how they are connected as part of a system of other concepts. An obvious approach to analysing scientific knowledge is to focus on terms that stand for the concepts, and on how relationships between the terms emerge on different contexts These relationships form a networked lexicon of terms and names, where the connections between them derive from contextualised instances of how the terms are used and how situations are named (Kuhn 2000). The assumption that concept’s meaning is related to the lexical system of terms is supported by recent advances in understanding how the meaning of ordinary concepts builds up through interlinked connections (Stella et al 2017; Vitevich and Castro 2015) According to these studies difficulties and deficiencies in learning the meaning of words are directly reflected in the relational structure of the lexical networks, especially in the local and global connectivity of words in the network. The results of these studies show the importance of the relational connections between words in learning their meaning and how certain key words play a special role in learning the lexicons (Stella et al 2017; Vitevich and Castro 2015)
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