Detecting At-Risk Students on Computer Science Bachelor Programs Based on Pre-Enrollment Characteristics

Autor/inn/en	Varga, Erika B.; Sátán, Ádám
Titel	Detecting At-Risk Students on Computer Science Bachelor Programs Based on Pre-Enrollment Characteristics
Quelle	In: Hungarian Educational Research Journal, 11 (2021) 3, S.297-310 (14 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Varga, Erika B.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2064-2199
Schlagwörter	Identification; At Risk Students; Computer Science Education; Undergraduate Students; Student Characteristics; Predictor Variables; College Faculty; Foreign Countries; Dropouts; Programming; Academic Achievement; Hungary + Suchen Sie Ihr Suchwort? Identifikation; Identifizierung; Computer science lessons; Informatikunterricht; Prädiktor; Fakultät; Ausland; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Programmierung; Schulleistung; Ungarn
Abstract	The purpose of this paper is to investigate the pre-enrollment attributes of first-year students at Computer Science BSc programs of the University of Miskolc, Hungary in order to find those that mostly contribute to failure on the Programming Basics first-semester course and, consequently to dropout. Our aim is to detect at-risk students early, so that we can offer appropriate mentorship program to them. The study is based on secondary school performance and first-semester Programming Basics course results from the last decade of over 500 students. Secondary school performance is characterized by the rank of the school, admission point score, and foreign language knowledge. The correlation of these data with the Programming Basics course result is measured. We have tested three hypotheses, and found that admission point score and school rank together have significant impact on the first-semester Programming Basics course results. The findings also support our assumption that students having weaknesses in all examined pre-enrollment attributes are subject to dropout. This paper presents our analysis on students' data and the method we used to determine the attributes that mostly affect dropout. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01