Sameer Khan
Papers
1
Total Citations
4
H-Index
1
About
Sameer Khan is a researcher in educational data mining (EDM), focusing on how machine learning can uncover the hidden factors that shape student success. His most-cited work, a 2021 review on feature selection methods for improving classification performance in EDM, has garnered 4 citations and serves as a foundational guide for the field. In this paper, Khan systematically evaluates how selecting the most relevant data attributes can dramatically enhance the accuracy of predictive models—helping educators identify at-risk students and make data-driven decisions. His contributions lie at the intersection of data science and pedagogy, providing practical frameworks for transforming raw academic records into actionable insights. By addressing the critical challenge of feature selection, Khan’s work directly supports the development of more efficient and interpretable educational tools. His research is particularly valuable for institutions seeking to personalize learning and improve retention rates. With a clear focus on bridging technical methodology and real-world educational outcomes, Sameer Khan is establishing himself as a thoughtful voice in the growing field of learning analytics.
Research Focus
Key Achievements
Top Papers
- 1