Applications of Network Science to Education Research: Quantifying Knowledge and the Development of Expertise through Network Analysis

Autor/in	Siew, Cynthia S. Q.
Titel	Applications of Network Science to Education Research: Quantifying Knowledge and the Development of Expertise through Network Analysis
Quelle	In: Education Sciences, 10 (2020), Artikel 101 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Siew, Cynthia S. Q.)
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2227-7102
Schlagwörter	Expertise; Network Analysis; Cognitive Psychology; Concept Formation; Instructional Design; Cognitive Processes; Teaching Methods; Measurement; Knowledge Level; Educational Research + Suchen Sie Ihr Suchwort? Expert appraisal; Netzplantechnik; Kognitive Psychologie; Concept learning; Begriffsbildung; Lesson concept; Lessonplan; Unterrichtsentwurf; Cognitive process; Kognitiver Prozess; Teaching method; Lehrmethode; Unterrichtsmethode; Messverfahren; Wissensbasis; Bildungsforschung; Pädagogische Forschung
Abstract	A fundamental goal of education is to inspire and instill deep, meaningful, and long-lasting conceptual change within the knowledge landscapes of students. This commentary posits that the tools of network science could be useful in helping educators achieve this goal in two ways. First, methods from cognitive psychology and network science could be helpful in quantifying and analyzing the structure of students' knowledge of a given discipline as a knowledge network of interconnected concepts. Second, network science methods could be relevant for investigating the developmental trajectories of knowledge structures by quantifying structural change in knowledge networks, and potentially inform instructional design in order to optimize the acquisition of meaningful knowledge as the student progresses from being a novice to an expert in the subject. This commentary provides a brief introduction to common network science measures and suggests how they might be relevant for shedding light on the cognitive processes that underlie learning and retrieval, and discusses ways in which generative network growth models could inform pedagogical strategies to enable meaningful long-term conceptual change and knowledge development among students. (As Provided).
Anmerkungen	MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. Tel: e-mail: indexing@mdpi.com; Web site: http://www.mdpi.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01