Decision Tree for PLOs of Undergraduate Computing Program Based on CLO of Computer Programming

Autor/in	Shaheen, Muhammad
Titel	Decision Tree for PLOs of Undergraduate Computing Program Based on CLO of Computer Programming
Quelle	In: Interactive Learning Environments, 31 (2023) 4, S.2452-2470 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Shaheen, Muhammad)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2021.1890621
Schlagwörter	Undergraduate Students; Programming; Computer Science Education; Educational Objectives; Outcomes of Education; Computer Software; Correlation; Classification; Decision Making; Accuracy; Learning Analytics; Taxonomy; Program Evaluation + Suchen Sie Ihr Suchwort? Programmierung; Computer science lessons; Informatikunterricht; Educational objective; Bildungsziel; Erziehungsziel; Lernleistung; Schulerfolg; Korrelation; Classification system; Klassifikation; Klassifikationssystem; Decision-making; Entscheidungsfindung; Taxonomie; Programme evaluation; Programmevaluation
Abstract	Outcome-based education (OBE) is uniquely adapted by most of the educators across the world for objective processing, evaluation and assessment of computing programs and its students. However, the extraction of knowledge from OBE in common is a challenging task because of the scattered nature of the data obtained through Program Educational Objectives (PEOs), Program Learning Outcomes (PLOs) and Course Learning Outcomes (CLOs). Computer programming correctly is the most demanded skill in the computing industry for which it is included in the CLOs of many computing courses at diverse levels of Bloom's taxonomy. This study is typically aimed to develop a decision tree classifier for the comprehensive assessment of PLOs in direct correlation with the CLOs related to computer programming. The PLOs of a computing program defined by Seoul Accord are correlated by using Spearman ranked correlation with the programming-related CLOs and mapped on a C4.5 decision tree to rank the importance of different metrics for objective learning. For the testing of the method the data were collected for two semesters of the academic BSCS program of Fall-2016 and Fall-2017 intake. The results obtained through decision tree classifier are more accurate than the conventionally practiced method. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01