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

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Title: Prediction of an Educational Institute Learning Environment Using Machine Learning and Data Mining
Language: English
Authors: Shoaib, Muhammad, Sayed, Nasir, Amara, Nedra, Latif, Abdul, Azam, Sikandar, Muhammad, Sajjad
Source: Education and Information Technologies. Aug 2022 27(7):9099-9123.
Availability: 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/
Peer Reviewed: Y
Page Count: 25
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Descriptors: Prediction, Artificial Intelligence, Student Behavior, Academic Achievement, Data Collection, Data Analysis, Pattern Recognition, Educational Environment
DOI: 10.1007/s10639-022-10970-4
ISSN: 1360-2357
1573-7608
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.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1347176
Database: ERIC
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  Data: 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/
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  Data: 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.
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