Application of Machine Learning and Data Mining in Predicting the Performance of Intermediate and Secondary Education Level Student

Autor/inn/en	Yousafzai, Bashir Khan; Hayat, Maqsood; Afzal, Sher
Titel	Application of Machine Learning and Data Mining in Predicting the Performance of Intermediate and Secondary Education Level Student
Quelle	In: Education and Information Technologies, 25 (2020) 6, S.4677-4697 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-020-10189-1
Schlagwörter	Artificial Intelligence; Foreign Countries; Prediction; Grades (Scholastic); Data Analysis; Data Use; Educational Quality; Secondary School Students; Pakistan + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Ausland; Vorhersage; Notenspiegel; Auswertung; Quality of education; Bildungsqualität; Sekundarschüler
Abstract	The presented work is a student marks and grade prediction system using supervised machine learning techniques, the system is developed on the historic performance of students. The data used in this research is collected from Federal Board of Intermediate and Secondary Education Islamabad Pakistan, there are 7 regions in FBISE i.e. Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan, Azad Jammu and Kashmir and overseas. The aims of this work is to analyze the education quality which is closely tightened with the sustainable development goals. The implementation of the system has produced an excess of data which must be processed suitably to gain more valuable information that can be more useful for future development and planning. Student marks and grade prediction from their historic academic data is a popular and useful application in educational data mining, so it is becoming a valuable source of information which can be used in different manners to improve the education quality in the country. Related work shows that several method for academic grade prediction are developed for the betterment of teaching and administrative staff of an educational organizational system. In our proposed methodology, the obtained data is preprocessed to improve the quality of data, the labeled student historic data (29 optimal attributes) is used to train decision tree classifier and regression model. The classification system will predict the grade while the regression model will predict the marks, finally the results obtained from both the model are analyzed. The obtain results show the effectiveness and importance of machine learning technology in predicating the students performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01