Learning Theories Use and Relationships in Computing Education Research

Autor/inn/en	Szabo, Claudia; Sheard, Judy
Titel	Learning Theories Use and Relationships in Computing Education Research
Quelle	In: ACM Transactions on Computing Education, 23 (2023) 1, Artikel 5 (34 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Szabo, Claudia)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Learning Theories; Computer Science Education; Educational Research; Research and Development; Theory Practice Relationship + Suchen Sie Ihr Suchwort? Learning theory; Lerntheorie; Computer science lessons; Informatikunterricht; Bildungsforschung; Pädagogische Forschung; Forschung und Entwicklung; Theorie-Praxis-Beziehung
Abstract	The use of established and discipline-specific theories within research and practice is an indication of the maturity of a discipline. With computing education research as a relatively young discipline, there has been recent interest in investigating theories that may prove foundational to work in this area, with discipline-specific theories and many theories from other disciplines emerging as relevant. A challenge for the researcher is to identify and select the theories that provide the best foundation for their work. Learning is a complex and multi-faceted process and, as such, a plethora of theories are potentially applicable to a research problem. Knowing the possible candidate theories and understanding their relationships and potential applicability, both individually or as a community of theories, is important to provide a comprehensive grounding for researchers and practitioners alike. In this work, we investigate the fundamental connections between learning theories foundational to research and practice in computing education. We build a comprehensive list of 84 learning theories and their source and influential papers, which are the papers that introduce or propagate specific theories within the research community. Using Scopus, ACM Digital Library, and Google Scholar, we identify the papers that cite these learning theories. We subsequently consider all possible pairs of these theories and build the set of papers that cite each pair. On this weighted graph of learning theory connections, we perform a community analysis to identify groups of closely linked learning theories. We find that most of the computing education learning theories are closely linked with a number of broader learning theories, forming a separate cluster of 17 learning theories. We build a taxonomy of theory relationships to identify the depth of connections between learning theories. Of the 294 analysed links, we find deep connections in 32 links. This indicates that while the computing education research community is aware of a large number of learning theories, there is still a need to better understand how learning theories are connected and how they can be used together to benefit computing education research and practice. (As Provided).
Anmerkungen	Association for Computing Machinery. 2 Penn Plaza Suite 701, New York, NY 10121. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01