Modeling Different Variables in Learning Basic Concepts of Programming in Flipped Classrooms

Autor/in	Durak, Hatice Yildiz
Titel	Modeling Different Variables in Learning Basic Concepts of Programming in Flipped Classrooms
Quelle	In: Journal of Educational Computing Research, 58 (2020) 1, S.160-199 (40 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0735-6331
DOI	10.1177/0735633119827956
Schlagwörter	Educational Technology; Technology Uses in Education; Programming; Teaching Methods; Video Technology; Homework; College Freshmen; Computer Science Education; Achievement; Predictor Variables; Foreign Countries; Problem Solving; Cognitive Processes; Turkey + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Programmierung; Teaching method; Lehrmethode; Unterrichtsmethode; Hausaufgabe; Studienanfänger; Computer science lessons; Informatikunterricht; Performance; Leistung; Prädiktor; Ausland; Problemlösen; Cognitive process; Kognitiver Prozess; Türkei
Abstract	Learning the basic concepts of programming and its foundations is considered as a challenging task for students to figure out. It is a challenging process for lecturers to learn these concepts, as well. The current literature on programming training abounds with the examples of a wide range of methods employed. Within this context, one of the prominent approaches in programming training is flipped classroom (FC) model. This article has sought to illuminate the effect of cognitive flexibility, problem-solving skills (PSS), and flipped learning readiness (FLR) levels on students' programming achievements in programming training through FC model. A total of 149 freshmen computer science students studying in a state university in Turkey were recruited for this study. In this study, designed as a relational screening model, a personal form, an achievement test, and three different data collection instruments were employed to collect data. For the data analysis, structural equation modeling, a multivariate statistical analysis technique, was used to reveal a model explaining and predicting the relations between programming achievement and different variables. The findings clearly indicate that FLR is the most important predictor of the programming achievements of students in FC. Other important predictors were found as PSS and cognitive flexibility. The research model demonstrates that an increase or development in FLR, PSS, and cognitive flexibility levels in FC will enhance the achievements of students in programming. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01