Structural Analysis of Factual, Conceptual, Procedural, and Metacognitive Knowledge in a Multidimensional Knowledge Network

Abstract

Discovering the most suitable network structure of the learning domain represents one of the main challenges of knowledge delivery and acquisition. We propose a multidimensional knowledge network (MKN) consisting of three components: multilayer network and its two projections. Each network layer constitutes factual, conceptual, procedural, or metacognitive knowledge within the domain of databases as a standard course of computer science study. In the MKN layer, nodes are concepts or knowledge units and the edges are weighted with regard to Bloom's cognitive learning level. The projected network layers are contrasted with a monolayer network by comparing characterizations of the centrality measures: degree centrality, closeness centrality, betweenness centrality, and eccentricity. The study revealed indications of how concepts, supported with the higher number of previously introduced concepts, have a dominant role in knowledge acquisition, from a view of knowledge structure and content. The analysis of communities, assortativity coefficient, and overlap between MKN layers contributes to better structuring of knowledge. MKN enables systematic insights into the efficiency of knowledge integration across metacognitive layers, as well as the detection of crucial cognitive concepts that reduce/increase the cognitive load during learning.

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.…”

An Autocatalytic Network Model of Conceptual Change

“…Another area relevant to understanding the emergence of conceptual structure is the application of network science to creativity (Benedek et al., 2017; Benedek & Neubauer, 2013; Kenett & Faust, 2019; Kenett et al., 2018). This research focuses on explaining cognitive processing using an underlying concept network, for example, the combining of knowledge representations to generate novel combinations and solutions (Benedek et al., 2017; Benedek & Neubauer, 2013; Kenett et al., 2018; Kenett & Faust, 2019; Vukić, Martinčić‐Ipšić, & Meštrović, 2020). This research has brought to light a wealth of interesting findings, such as that high creative, high intelligence individuals have more richly connected concept graphs, suggesting that they navigate conceptual space more effectively (Benedek et al., 2017; Kenett et al., 2018).…”

Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process

“…However, much less has been said about how modern tools of network science have advanced, and continue to advance, our understanding of human cognition, which is our focus here. Another related issue to note is that the cognitive and language networks we describe below are conceptually quite similar to knowledge graphs commonly analysed in the domains of computer science [31] and learning and educational analytics [32]. In these areas, nodes represent cognitive units as well, although these knowledge graphs focus on summarizing large amounts of information for efficient search and retrieval processes in a database, which could potentially be analogous to human search and retrieval [33].…”