Assessing the Tendency of Judging Bias in Student Competition: A Data Mining Approach

Autor/inn/en	Ulkhaq, M. Mujiya; Pramono, Susatyo N. W.; Adyatama, Arga
Titel	Assessing the Tendency of Judging Bias in Student Competition: A Data Mining Approach
Quelle	In: Journal of Applied Research in Higher Education, 15 (2023) 4, S.1198-1211 (14 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ulkhaq, M. Mujiya)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2050-7003
DOI	10.1108/JARHE-02-2022-0053
Schlagwörter	Competition; College Students; Foreign Countries; Judges; Bias; Information Retrieval; Data Analysis; Indonesia + Suchen Sie Ihr Suchwort? Wettkampf; Collegestudent; Ausland; Judge; Richter; Auswertung; Indonesien
Abstract	Purpose: Judging bias is ironically an inherent risk in every competition, which might threaten the fairness and legitimacy of the competition. The patriotism effect represents one source of judging bias as the judge favors contestants who share the same sentiments, such as the nationalistic, racial, or cultural aspects. This study attempts to explore this type of judging bias in a university student competition. In addition, this study tries to expand the literature on judging bias by proposing the term universitarian bias as the judge is suspected to give a higher score to contestants from the same university. Design/methodology/approach: The association rule of data mining is used to accomplish the objective of the study. To demonstrate the applicability of the method, the data set from the annual national university student competition in Indonesia is exploited. Findings: The result strongly discovers that the universitarian bias is likely to be present. Some recommendations are also provided in order to minimize the bias that might happen again in the future. Practical implications: As the implication of the presence of the universitarian bias, the committee should remove all the university features attributed to the participants. This endeavor is expected to minimize the universitarian bias that might happen. Originality/value: This research is claimed to be the first attempt in implementing the data mining technique in the field of judging bias. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01