Statistical Modeling of Students' Academic Performances: A Longitudinal Study

Autor/inn/en	Kemda, Lionel Establet; Murray, Michael
Titel	Statistical Modeling of Students' Academic Performances: A Longitudinal Study
Quelle	In: International Journal of Higher Education, 10 (2021) 6, S.153-170 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1927-6044
Schlagwörter	Academic Failure; Student Behavior; College Students; Student Attrition; Outcomes of Education; Student Financial Aid; Race; Gender Differences; Academic Achievement; Dropouts; Admission (School); Foreign Countries; Statistical Analysis; Models; South Africa + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Collegestudent; Schülerbeurlaubung; Lernleistung; Schulerfolg; Finanzielle Beihilfe; Studienfinanzierung; Studienförderung; Rasse; Abstammung; Geschlechterkonflikt; Schulleistung; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Ausland; Statistische Analyse; Analogiemodell; Südafrika; Süd-Afrika; Republik Südafrika; Südafrikanische Republik
Abstract	Within students' attrition studies, it is necessary to assess the longitudinal evolution of students within a given course of study, from enrolment to exit from the university through degree completion and academic dropout. Here, the student's academic progress is monitored through the number of courses failed each semester enrolled. The students' failure rate and academic behavior typically provide significant insight into students' exit outcomes from University programs. These programs usually have a maximum time frame required to complete the course. A likelihood-based approach is discussed that conditions on the exit outcome and random effects in adjusting within-subject correlation of longitudinal measurements. Ignoring the number of courses enrolled by a student may produce inadequate results on the actual failure rates. Conditioned on the exit outcomes of the student, we find out that factors such as financial aid, matriculation points, students' race and course type registered, and gender are distinguishing factors that affect students' academic performances, for completers and dropouts. Also, being in university-type accommodation (that often have added services such as transportation and internet connections) does not seem to significantly affect the failure rate within both groups of students. In addition, an increase in matriculation points significantly reduces the failure rate independent of the Quintile school of the student. Several count models such as mixed Poisson, mixed Zero Inflation Poisson, mixed Negative Binomial, and mixed Hurdle Poisson models are fitted and compared. In particular, the mixed Poisson model provides a better fit based on the Bayesian Information Criterion and residues analysis. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01