A Bayesian Performance Prediction Model for Mathematics Education: A Prototypical Approach for Effective Group Composition

Autor/inn/en	Bekele, Rahel; McPherson, Maggie
Titel	A Bayesian Performance Prediction Model for Mathematics Education: A Prototypical Approach for Effective Group Composition
Quelle	In: British Journal of Educational Technology, 42 (2011) 3, S.395-416 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/j.1467-8535.2009.01042.x
Schlagwörter	Foreign Countries; Personality Traits; Mathematics Education; Prediction; Mathematics Instruction; High School Students; Mathematics Achievement; Predictive Measurement; Student Characteristics; Automation; Models; Computer Uses in Education; Correlation; Bayesian Statistics; Grouping (Instructional Purposes); Ethiopia + Suchen Sie Ihr Suchwort? Ausland; Individual characteristics; Personality characteristic; Persönlichkeitsmerkmal; Mathematische Bildung; Vorhersage; Mathematics lessons; Mathematikunterricht; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Analogiemodell; Computernutzung; Korrelation; Grouping; Gruppenbildung; Äthiopien
Abstract	This research work presents a Bayesian Performance Prediction Model that was created in order to determine the strength of personality traits in predicting the level of mathematics performance of high school students in Addis Ababa. It is an automated tool that can be used to collect information from students for the purpose of effective group composition. During the study, attributes that affect performance in mathematics were identified and the sources of the data were analysed. Based on collected data, a predictive tool was developed and a 70.9% prediction accuracy was achieved vis-a-vis actual exam results. Further work and modification of the prediction model increased the level of prediction accuracy to 78.4%. The findings of this research provide an insight into the possible applications of uncertainty management techniques, particularly to address some of the conflicting results relating to the significance of factors affecting student performance of the mathematics subject. This has implications regarding methods of grouping learners for optimum performance. Future research may include a deeper exploration of factors related to performance and more specifically how the Bayesian predictive model can be incorporated for effective group formation. Furthermore, researchers might be able to extend the application of similar performance prediction models for other academic subjects. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10