Machine Learning Algorithms for Student Success Prediction in Higher Education
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Overview
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Explore a 25-minute conference talk from Data Science Conference Europe 2022 that delves into the application of machine learning algorithms for predicting student academic success in higher education. Learn how predictive models analyze data from 400 IT students across three generations to identify key factors influencing university performance. Discover insights into generational differences, the impact of student responsibility and class attendance on academic outcomes, and the significant potential of machine learning in developing effective predictive models for educational institutions. Through detailed analysis including first grade statements, correlation studies, and sensitivity analysis results, gain comprehensive understanding of how data science can be leveraged to support early intervention strategies for at-risk students and ultimately enhance the quality of higher education.
Syllabus
Introduction
Context
Brief Context
Machine Learning Algorithms
Framework
Research Papers
Literature Review
Data Source
Results
First Grade
Statement
Data
Graphs
YouTube Analytics
Sensitivity Analysis Results
Correlation Analysis
Conclusion
Future work
Research questions
Questions
Taught by
Data Science Conference