Graduate student mines educational data to boost students’ learning

Meena Rapaka is a graduate student studying big data

Master’s student Meena Rapaka works with a research team in Mason Engineering’s Personalized Learning in Applied Information Technology Laboratory. They apply advanced analytic techniques to educational data.

Master’s student Meena Rapaka came to Mason to learn more about big data and ended up doing data-mining research that helps others succeed in their studies.

For two years, she has been a research assistant in Mason Engineering’s Personalized Learning in Applied Information Technology Laboratory.  

“We strive to improve the learning experiences and expediency of students taking challenging STEM (science, technology, engineering, and math) courses,” says Rapaka, who’s studying applied information technology in the Department of Information Sciences and Technology (IST).

The lab, under the guidance of co-directors Ioulia Rytikova and Mihai Boicu, focuses on developing innovative teaching techniques to support research-based, student-centered active learning environment in the classroom. Active learning involves teaching students through engaging, creative activities instead of the typical lecture method.

Rapaka researched ways to help educators get a better understanding of what impacts students’ studies, including identifying the emotional impact of learning and the causes of failure.

“I conducted data analysis and used various algorithms to examine educational data to search for interesting patterns and hidden relationships in data that would help understand and improve students’ performance,” she says.

Some key findings from the lab’s recent research:

  • Inquiry-based learning increases students’ curiosity and independent thinking.
  • Active learning increases students’ engagement and motivation in the classroom.
  • Personalized learning and individual mentoring help students take ownership of their skill development.

Rapaka, whose concentration is in data analytics and intelligence methods, says having hands-on research experiences has been a boon. “I enjoyed working with a team of other research students, applying advanced analytic techniques, such as artificial intelligence, machine learning, and deep learning concepts, to the data.”

She also served as a graduate teaching assistant for Rytikova, an associate professor in IST “Meena helped successfully incorporate educational techniques from our research into the classroom,” Rytikova says.

Rapaka was a software developer in India before she decided to come to Mason to develop her expertise in big data.

After graduating in May, she wants to work as a data scientist or data analyst. “I am currently looking for job opportunities where I can put what I have learned to use and explore the world of big data.”