Concept Mapping in Magnetism and Electrostatics: Core Concepts and Development over Time

Abstract

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.

Comprehension-Oriented Learning of Cell Biology: Do Different Training Conditions Affect Students’ Learning Success Differentially?

“…The structural model of cognition from McClelland et al [81] considers relevant (prior) knowledge activated during information processing as a central element of information integration. However, this prior knowledge can contradict the knowledge to be acquired, i.e., learners' prior knowledge inhibits learning, as new information cannot be meaningfully integrated into the existing network [82]. For educators, this means that the influence of learners' prior knowledge on learning activities must be considered in terms of activating corresponding processes as precisely as possible when dealing with new tasks [83][84][85][86][87][88].…”

Systemic States of Spreading Activation in Describing Associative Knowledge Networks: From Key Items to Relative Entropy Based Comparisons

“…In that picture, parameter q can be related to the degree of non-extensivity, often originating, e.g., from fractality or compartmentalization of the system [24]. However, deeper discussion is not necessary here, and we can treat q simply as parameter controlling the non-linearity in Equation (2). However, to connect the q-generalized state to Tsallisentropy, we allow the q-index q * for Tsallis-entropy to differ from value of q in Equations ( 2) and (3), and only afterwards, fix the connection between the two indices.…”

“…Knowledge that is central in learning and teaching often starts with factual knowledge of agents, objects, and events, and how they are related. This is equally true in such different areas of learning and instruction as, for example, in physics [1][2][3] and history [4,5]. The familiarization of new key concepts involves making connections with what is already known and how new items and terms can be integrated into more extensive knowledge structures [6][7][8].…”

Systemic States of Spreading Activation in Describing Associative Knowledge Networks II: Generalisations with Fractional Graph Laplacians and q-Adjacency Kernels

“…Finally, we note that recent research in learning and education utilizing network approaches offer many similar educational contexts of applications as discussed here (see, e.g., [47,48] and references therein). In particular, networks that are very similar to the AKN studied here have recently been examined in learning physics [49][50][51][52], chemistry [53], computer science and statistics [54,55] as well as in learning psychology and education [56,57] and the history of science [58,59]. In these cases, network measures used in analysis have been conventional static and local centrality measures.…”