Concept networks in learning: finding key concepts in learners' representations of the interlinked structure of scientific knowledge

Koponen, Ismo T.; Nousiainen, Maija

doi:10.1093/comnet/cnu003

Cited by 32 publications

(66 citation statements)

References 11 publications

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“…The key concepts and how they change when epistemic weighting is increased are shown in Fig. 4 as a fingerprint -map (Koponen and Nousiainen 2014). The map shows the key concepts of high in-or out-communicability as black stripes, and the lesser the communicability is the lighter is the colour of the stripe.…”

Section: The Key Conceptsmentioning

confidence: 99%

Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support

Koponen

Nousiainen

2018

Appl Netw Sci

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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 outgoing and incoming 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 subset of these key concepts but with a limited overlap of the subsets with other individual networks. The epistemically well substantiated knowledge is thus sparsely distributed over the 12 individual networks.

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Section: The Key Conceptsmentioning

confidence: 99%

Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support

Koponen

Nousiainen

2018

Appl Netw Sci

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“…Some notable and important exceptions, however, state their goals by using notions of coherence of knowledge or conceptual coherence [12][13][14][15]. Addressing such structural features requires that students have expressed their knowledge either in the form of relational systems, e.g., concept maps [8][9][10][11][16][17][18], or in a form which yields to analysis of relational connections or clusters of connections [12][13][14][15]. Recent research exploring the characteristics of novices' and experts' knowledge has, however, devoted closer attention to better defining the structure of interest in regard students' knowledge, explicitly and quantitatively, as local cohesion and global connectivity concepts [19][20][21][22] or connected paths between concepts [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Addressing such structural features requires that students have expressed their knowledge either in the form of relational systems, e.g., concept maps [8][9][10][11][16][17][18], or in a form which yields to analysis of relational connections or clusters of connections [12][13][14][15]. Recent research exploring the characteristics of novices' and experts' knowledge has, however, devoted closer attention to better defining the structure of interest in regard students' knowledge, explicitly and quantitatively, as local cohesion and global connectivity concepts [19][20][21][22] or connected paths between concepts [16][17][18]. In these studies, structural notions are reduced to better definable notions of local cohesion and global (contiguous) connectivity of knowledge elements, which can be given both proper operationalization and used as the basis for network metrics [17,18,20,23].…”

Section: Introductionmentioning

confidence: 99%

Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism

Koponen

Nousiainen

2019

Education Sciences

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Relational interlinked dependencies between concepts constitute the structure of abstract knowledge and are crucial in learning conceptual knowledge and the meaning of concepts. To explore pre-service teachers’ declarative knowledge of physics concepts, we have analyzed concept networks, which agglomerate 12 pre-service teacher students’ representations of the key elements in electricity and magnetism. We show that by using network-based methods, the interlinked connections of nodes, locally and globally, can be analyzed to reveal how different elements of the network are supported through their connections to other nodes in the network. Nodes with high global connectivity initialize contiguous concept patchworks within the network and are thus most often found to be abstract, general, and advanced concepts. Locally cohesive concepts, on the other hand, are nearly always auxiliary supporting concepts, related to specific textbook-type experiments and model-type conceptional elements. Comparisons of group-level knowledge and individual pre-service teacher students’ knowledge in the form of networks shows that while in group-level the aggregated knowledge is expert-like, at the individual level pre-service teacher students possess only a fraction of that knowledge.

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“…For instance, Sherin [40] used spoken word transcripts to identify students' concepts and the dynamics of their mental constructs (by means of automated analyses using vector space models and simple clustering methods). Koponen and Huttunen [25] or Koponen and Nousiainen [41] proposed possibilities to shed light on conceptual dynamics with the help of network-and graph-related analyses. Bodin [24] used network analyses to map students' epistemic framing of computational physics or to characterize teachers' and students' simulation competence in physics.…”

Section: Network Analyses Of Conceptual Knowledgementioning

confidence: 99%

“…Koponen and Huttunen [25] explicitly address the coherence vs. fragmentation issue based on network parameters by modeling a concept's coherence and utility in a given situation. Koponen and Nousiainen [41] furthermore suggest parameters to estimate the importance of single conceptual elements within a given network.…”

Section: Network Analyses Of Conceptual Knowledgementioning

confidence: 99%

Investigating Network Coherence to Assess Students’ Conceptual Understanding of Energy

Podschuweit

Bernholt

2020

Education Sciences

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Conceptual knowledge is a crucial tool for students to understand scientific phenomena. Knowledge about the structure and function of mental concepts potentially helps science educators to foster the acquisition of this tool. Specifically, the coherence of students' mental concepts is an intensely discussed issue within the related conceptual change discourse. While former discussions focused on the question of whether these conceptions are coherent or not, recent approaches describe them as dynamic systems behaving more or less coherently in different situations. In this contribution, we captured this dynamic behavior of individual concepts by means of network analysis. Transcribed video data of 16 pairs of students working on four subsequent experiments on energy were transformed into weighted networks, which in turn were characterized by standardized coherence parameters. These coherence parameters and more basic network parameters were correlated with students' pre-post scores of a multiple-choice test on the energy concept. We found that the coherence parameter is significantly related to the students' test scores. Even more intense relations are indicated if networks are calculated solely based on conceptual key terms. Implications as well as methodological constraints of this approach are discussed.

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Concept networks in learning: finding key concepts in learners' representations of the interlinked structure of scientific knowledge

Cited by 32 publications

References 11 publications

Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support

Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support

Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism

Investigating Network Coherence to Assess Students’ Conceptual Understanding of Energy

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