Maryam Zaffar
Papers
1
Total Citations
4
H-Index
1
About
Maryam Zaffar is a researcher whose work sits at the intersection of data science and education, with a primary focus on educational data mining (EDM). Her most-cited paper, a 2021 review on feature selection methods for improving classification performance in EDM, has garnered 4 citations and provides a critical framework for discovering the key factors that influence student academic performance. By systematically evaluating how to identify the most meaningful data points, Zaffar’s work helps educational managers make more informed, data-driven decisions. Her contributions are particularly valuable in an era where institutions seek to leverage student data to predict outcomes and personalize learning. Beyond this foundational review, Zaffar’s research continues to explore how machine learning techniques can be refined to better serve educational contexts, ensuring that predictive models are both accurate and interpretable. Her work stands as a practical guide for researchers and practitioners aiming to improve student success through rigorous, evidence-based analysis.
Research Focus
Key Achievements
Top Papers
- 1