Prediction of an Educational Institute Learning Environment Using Machine Learning and Data Mining

Autor/inn/en	Shoaib, Muhammad; Sayed, Nasir; Amara, Nedra; Latif, Abdul; Azam, Sikandar; Muhammad, Sajjad
Titel	Prediction of an Educational Institute Learning Environment Using Machine Learning and Data Mining
Quelle	In: Education and Information Technologies, 27 (2022) 7, S.9099-9123 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-10970-4
Schlagwörter	Prediction; Artificial Intelligence; Student Behavior; Academic Achievement; Data Collection; Data Analysis; Pattern Recognition; Educational Environment + Suchen Sie Ihr Suchwort? Vorhersage; Künstliche Intelligenz; Student behaviour; Schülerverhalten; Schulleistung; Data capture; Datensammlung; Auswertung; Mustererkennung; Lernumgebung; Pädagogische Umwelt; Schulumwelt
Abstract	Technology and data analysis have evolved into a resource-rich tool for collecting, researching and comparing student achievement levels in the classroom. There are sufficient resources to discover student success through data analysis by routinely collecting extensive data on student behaviour and curriculum structure. Educational Data Mining (EDM), a method of data analysis in the learning environment, has emerged as an emerging trend in the development of educational data mining and analysis techniques. EDM aids in the comprehension of student behaviour as well as the factors that influence student behaviour and achievement. Student learning patterns, student culture, and instructional skills are all important factors in a successful study of EDM students. This study will look at how technology and data mining are used in the EDM environment and compare the results. We have used previous research to determine which method is best for observing the learning environment and what factors influence student academic performance. Two state-of-the-art models i.e. decision tree (classifier) and DBSCAN (clustering method) are used to predict the performance of an educational institute with higher accuracy. (As Provided).
Anmerkungen	Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01