The network approach to assess the structure of knowledge: Storage, distribution and retrieval as three measures in analysing concept maps

Kapuza, Anastasiya; Koponen, Ismo T.; Tyumeneva, Yulia

doi:10.1111/bjet.12938

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…In addition, Clariana et al ( 2013 ) used the centrality index of concept maps to measure the knowledge structure of learners in their study of computer-supported collaborative problem solving. Kapuza et al ( 2020 ) proposed three measures based on the basic structure index of the concept map to analyze the concept map generated during the statistical data analysis course to understand the knowledge structure of learners.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating online learners’ knowledge structure patterns by concept maps: A clustering analysis approach

Fang

Cheng

et al. 2023

Educ Inf Technol

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Section: Literature Reviewmentioning

confidence: 99%

Investigating online learners’ knowledge structure patterns by concept maps: A clustering analysis approach

Fang

Cheng

et al. 2023

Educ Inf Technol

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“…Students can associate 'work' with 'physics' through a single intermediate layer, which indicates a shorter network distance between these concepts compared to the more remote associations between "fun" and "physics". Since distance in cognitive networks corresponds with semantic relatedness [14,17] when domain knowledge is accounted for [13,36], this difference indicates that in students' mindset, "physics" is more related to "work" than "fun".…”

Section: Physics Maths and Workmentioning

confidence: 99%

Introducing mindset streams to investigate stances towards STEM in high school students and experts

Brian¹,

Stella²

2023

Preprint

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We introduce mindset streams for assessing ways of bridging two target concepts in concept maps. We focus on behavioural forma mentis networks (BFMN), which map the associative and affective dimensions of memory recalls. Inspired by trains of thoughts taking several paths to link ideas, mindset streams are defined as BFMN subgraphs induced by all shortest paths between two target concepts, e.g. all recalls in shortest paths bridging "math" and "learning". These streams quantify the following features of the mindset encoded in a BFMN: (i) semantic content (i.e. which ideas mediate connections between targets?), (ii) valence coherence/conflict (i.e. are connections mediated by entwining ideas perceived negatively, positively or neutrally?), and (iii) semantic relevance (i.e. are the bridges between targets peripheral or central for the connectivity/betweenness of the BFMN?). We investigate mindset streams between "maths"/"physics" and key motivational aspects of learning ("fun","work","failure") in two BFMNs, encoding how 159 students and 59 experts perceived and associated concepts about Science Technology Engineering and Maths (STEM), respectively. Statistical comparisons against configuration models show that high schoolers bridge "maths" and "fun" only through overabundant levels of valence-conflicting associations, contrasting negatively perceived domain knowledge with peer-related positive experiences. This conflict is absent in the researchers' mindset stream, which rather bridges "math" and "fun" through positive, science-related associations. The mindset streams of both groups bridge "maths" and "physics" to "work" through mostly positive career-related jargon. Students' mindset streams of "failure" and "math"/"physics" are dominated by negative associations with test anxiety, whereas researchers integrate "failure" and "math"/"physics" in semantically richer and more positive contexts, denoting failure itself as a cornerstone of STEM learning. We discuss our findings and future research directions in view of relevant psychology/education literature.

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“…33 Nº 1 -2022 El subproceso de almacenamiento de conocimiento solo es posible una vez que dicho conocimiento haya sido creado, y consiste en diseñar e implementar los sistemas y mecanismos que permitan almacenar, actualizar y recuperar eficientemente el conocimiento generado (Costa y Monteiro, 2018), el cual debe ser accesible y fácil de usar por quienes lo necesiten en la institución. Dicho de modo claro, la eficaz distribución del conocimiento resulta ser una condición esencial para el subproceso de almacenamiento (Kapuza et al, 2020).…”

Section: Rodríguez-ponceunclassified

Relaciones entre las fases del proceso de gestión del conocimiento en unidades académicas

et al. 2022

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La presente investigación tiene como propósito explorar las relaciones existentes entre las diferentes fases de la gestión del conocimiento: crear, almacenar, compartir y aplicar conocimiento. Se realizó un estudio exploratorio con una muestra de 35 líderes de unidades académicas de cinco instituciones chilenas. Se efectuó un análisis econométrico empleando el software SPSS, descubriéndose así la existencia de un conjunto de relaciones estadísticamente significativas entre distintas fases del proceso analizado. En concreto, los hallazgos evidenciaron que la aplicación de conocimiento depende directamente de los subprocesos de crear y compartir conocimiento. A su vez, la fase de almacenar conocimiento depende directamente de la fase de crear conocimiento y es un subproceso significativo para explicar la fase de compartir conocimiento. Se concluye que la validación de las variables utilizadas, su fiabilidad, y la exploración de relaciones con soporte teórico pone en relieve la importancia de promover y cultivar una gestión del conocimiento de alta calidad en cada una de sus fases.

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The network approach to assess the structure of knowledge: Storage, distribution and retrieval as three measures in analysing concept maps

Abstract: He is teaching conceptual physics courses for pre-service physics teachers, and his research interests are focused on concept learning. In his research he uses and develops network methods and approaches for science education research.

Cited by 11 publications

References 25 publications

Investigating online learners’ knowledge structure patterns by concept maps: A clustering analysis approach

Investigating online learners’ knowledge structure patterns by concept maps: A clustering analysis approach

Introducing mindset streams to investigate stances towards STEM in high school students and experts

Relaciones entre las fases del proceso de gestión del conocimiento en unidades académicas

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