Assessing Algorithmic Thinking Skills in Relation to Gender in Early Childhood

Autor/inn/en	Kanaki, Kalliopi; Kalogiannakis, Michail
Titel	Assessing Algorithmic Thinking Skills in Relation to Gender in Early Childhood
Quelle	In: Educational Process: International Journal, 11 (2022) 2, S.44-59 (16 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Kanaki, Kalliopi) ORCID (Kalogiannakis, Michail)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2147-0901
Schlagwörter	Thinking Skills; Gender Differences; STEM Education; Equal Education; Educational Opportunities; Play; Interdisciplinary Approach; Constructivism (Learning); Sex Stereotypes; Computer Science Education; Game Based Learning; Grade 1; Grade 2; Elementary School Students; Foreign Countries; Computer Software; Usability; Construct Validity; Greece + Suchen Sie Ihr Suchwort? Denkfähigkeit; Geschlechterkonflikt; STEM; Bildungsangebot; Bildungschance; Spiel; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Computer science lessons; Informatikunterricht; School year 01; 1. Schuljahr; Schuljahr 01; School year 02; 2. Schuljahr; Schuljahr 02; Ausland; Griechenland
Abstract	Background/purpose: In terms of computational thinking core facets, algorithmic thinking is a key competency applicable not only in Computer Science but also in aspects of daily life. Considering the global phenomenon of gender stereotypes with regards to the academic and professional orientation in STEM fields, we focused on investigating the level of students' algorithmic thinking skills by gender in early childhood. This article provides evidence of research implemented under the umbrella of quantitative methodology, employing an innovative assessment tool constructed to meet the requirements of the study. The findings obtained could facilitate researchers and policymakers to support equity in learning opportunities, starting from the first stage of compulsory education. Materials/methods: The study aligns to the principles of quantitative research methodology. Its backbone is a digital platform of multidisciplinary, play-based, and constructivist character, which we implemented from scratch in order to satisfy the requirements of our research. Results: The findings of the study revealed that algorithmic thinking skills are not related to students' gender in early childhood. Conclusion: The findings of the study bring out that, at very young ages, the effect of gender stereotypes is not observable as far as students' algorithmic thinking skills are concerned. The implications of the study highlight the need to focus on the schooling stages that follow early childhood in order to tackle the gender gap in STEM fields. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01