Learning Analytics in Programming Courses: Review and Implications

Autor/inn/en	Omer, Uzma; Tehseen, Rabia; Farooq, Muhammad Shoaib; Abid, Adnan
Titel	Learning Analytics in Programming Courses: Review and Implications
Quelle	In: Education and Information Technologies, 28 (2023) 9, S.11221-11268 (48 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-023-11611-0
Schlagwörter	Programming; Learning Analytics; Educational Research; Data; Evaluation; Identification; Novices; Behavior Patterns; Introductory Courses; Research Reports + Suchen Sie Ihr Suchwort? Programmierung; Bildungsforschung; Pädagogische Forschung; Daten; Evaluierung; Identifikation; Identifizierung; Einführungskurs; Research report; Forschungsbericht
Abstract	Learning analytics (LA) is a significant field of study to examine and identify difficulties the novice programmers face while learning how to program. Despite producing notable research by the community in the specified area, rare work is observed to synthesize these research efforts and discover the dimensions that guide the future research of learning analytics in programming courses (LAPC). This work demonstrates review of the learning analytics research for initial level programming courses by exploring different types and sources of data used for LA, and evaluating some pertinent facets of reporting, prediction, intervention, and refinements exhibited in literature. Based on the reviewed aspects, a taxonomy of LAPC research has been proposed along with the associated benefits. The results reveal that most of the learning analytics studies in programming courses used assessment data, which is generated from conventional assessment processes. However, the analysis based on more granular level data covering the cognitive dimensions and concept specific facets could improve accuracies and reveal the precise aspects of learning. In addition, the coding analysis parameters can broadly be categorized into code quality and coding process. These categories can further be classified to present twenty-five sub-categories of coding parameters for analyzing the behaviors of novice programmers. Moreover, efforts are required for early identification of effective and ineffective behavioral patterns through performance predictions in order to deliver timely interventions. Lastly, this review emphasizes the integration of related processes to optimize the future research efforts of conducting the learning analytics research for programming courses. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01