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Lost in Translation: Reimagining the Machine Learning Life Cycle in Education

With the rapid proliferation of machine learning (ML) technologies in the education sphere, accelerated in part by ongoing public health crises, an urgent need exists to investigate how machine learning supports education principles and goals. I present qualitative insights from interviews with education domain experts, grounded in machine learning for education (ML4Ed) papers published in applied machine learning conferences over the past decade. Our central research goal is to critically examine whether the stated or implied societal objectives of these papers are aligned with the machine learning problem formulation, objectives, and interpretation of results.


  • SERENA WANG University of California - Berkeley, PhD Student

Serena Wang is a PhD student in Computer Science at University of California, Berkeley. Her research focuses on fostering positive long term societal impact of machine learning by rethinking ML algorithms and practices. She is supported by the NSF Graduate Research Fellowship. She has concurrently held a 20% collaboration with Google Research for the last five years, where she worked on making machine learning models more interpretable and controllable.

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