Applying Machine Learning in Science Education Research When, How, and Why?

Peter Wulff works in the field of physics education research and science education research. He developed machine learning-based models to automatically assess pre-service physics teachers’ written reflections as well as physics problem solving. This work was published in peer-reviewed science education journals and journals specifying on artificial intelligence research in education. His work was funded by the Federal Ministry of Education and Research, Germany, and foundations.

Marcus Kubsch works in the area of science education research. He has used machine learning methods to detect students' emotions in multimodal data and to automatically provide feedback for pre-service teachers’ homework assignments. He is currently leading two large research projects that investigate the potentials of machine learning to identify K-12 students’ trajectories in science and automatically provide individualized feedback.

Christina Krist conducts research in science education focused on the epistemologies and ethics guiding K-12 students’ participation in science practices. She has been the recipient of both Dissertation and Postdoctoral Fellowships from the NAEd/Spencer Foundation. She is currently the PI of an NSF-funded project developing new methodologies that leverage machine learning in qualitatively analyzing visual and audio features of classroom video data.