An Analysis of Java Programming Behaviors, Affect, Perceptions, and Syntax Errors among Low-Achieving, Average, and High-Achieving Novice Programmers

Autor/inn/en	Rodrigo, Ma. Mercedes T.; Andallaza, Thor Collin S.; Castro, Francisco Enrique Vicente G.; Armenta, Marc Lester V.; Dy, Thomas T.; Jadud, Matthew C.
Titel	An Analysis of Java Programming Behaviors, Affect, Perceptions, and Syntax Errors among Low-Achieving, Average, and High-Achieving Novice Programmers
Quelle	In: Journal of Educational Computing Research, 49 (2013) 3, S.293-325 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0735-6331
DOI	10.2190/EC.49.3.b
Schlagwörter	Programming; High Achievement; Introductory Courses; Qualitative Research; Statistical Analysis; Error Patterns; Low Achievement; Student Attitudes; Computer Science Education; Preferences; Personal Autonomy; Predictor Variables; Self Efficacy; Teacher Role; Teaching Methods; Concept Formation; Management Systems; Information Systems; Undergraduate Students; Foreign Countries; Philippines + Suchen Sie Ihr Suchwort? Programmierung; Einführungskurs; Qualitative Forschung; Statistische Analyse; Fehlertyp; Unterdurchschnittliche Leistung; Schülerverhalten; Computer science lessons; Informatikunterricht; Individuelle Autonomie; Prädiktor; Self-efficacy; Selbstwirksamkeit; Lehrerrolle; Teaching method; Lehrmethode; Unterrichtsmethode; Concept learning; Begriffsbildung; Ausland; Philippinen
Abstract	In this article we quantitatively and qualitatively analyze a sample of novice programmer compilation log data, exploring whether (or how) low-achieving, average, and high-achieving students vary in their grasp of these introductory concepts. High-achieving students self-reported having the easiest time learning the introductory programming topics. In a quantitative analysis, though, high-achieving and average students were: 1) more effective at debugging (on average, as quantified by Jadud's Error Quotient (EQ)) than low-achieving students; and 2) were least confused, as quantified using Lee's confusion metric. However, the differences in EQ and confusion between groups were not statistically significant. This implied that all groups struggled with programming to similar extents. This finding was further supported by was used to delineate two sets of variables. The results indicate that preference for autonomy in computer science learning positively predicts self-efficacy in learning computer science with the strongest coefficient. Computer science learner preference for teacher control is also a positive predictor. However, preference for participation in managing the computer class and preference for depending on the teacher did not play a significant role in the students' self-efficacy in learning computer science. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01