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Predicting Retention Rates from Students’ Behavior

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2020 ASEE Virtual Annual Conference Content Access


Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computing and Information Technology Division Technical Session 4

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Computing and Information Technology

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Paper Authors


Awatif Amin Johnson C. Smith University

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Awatif Amin is a computer science Assistant professor at Johnson C. Smith University scince 2001. She primarily focuses on programming and data analytics. She completed her Doctorate of Management in organizational Leadership with specialization in Information System Technology (DM/IST), She earned her B.S. and M.S. in Computer Science.

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Machine learning and Data mining are supported by the same establishments in two different ways. Machine learning basically learns from present data and delivers the necessary basis for a machine to learn. Data mining uses existing data and finds emerging patterns that help the decision-making processes. Data mining is typically used as an information supplier for machine learning to draw information that recognizes the patterns and determines from these patterns how to adapt behavior for future occurrences. It is obvious to see that there is an overlap between data mining and machine learning as the two have the same goal which is to learn from huge data for analytic resolutions. Machine learning and data mining can be considered knowledge science that concentrates on formulating algorithms that learn from the data and make predictions. Machine learning algorithms include supervised and unsupervised learning classifications. This paper deliberates the use of algorithms to analyze data from educational institutions to help them present more detailed methods to improve the efficiency of recruitment, enrollment, and hence retention.

Amin, A. (2020, June), Predicting Retention Rates from Students’ Behavior Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35073

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