Towards Building Child-Centered Machine Learning Pipelines: Use Cases from K-12 and Higher-Education

This paper proposes a framework for adapting machine learning pipelines to be child-centered and presents two case studies: predicting classroom engagement levels from video/biometric data to support teachers, and developing a handwriting recognition system for young learners with special educational needs.

Researchers and policy-makers have started creating frameworks and guidelines for building machine-learning (ML) pipelines with a human-centered lens. Machine Learning pipelines stand for all the necessary steps to develop ML systems (e.g., developing a predictive keyboard). On the other hand, a child-centered focus in developing ML systems has been recently gaining interest as children are becoming users of these products. These efforts dominantly focus on children's interaction with ML-based s

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