Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has…

Our study explores how US science education has evaluated multilingual students' languages as deficits and/or assets by comparing them against normative ideals. As a raciolinguistic genealogy, the study situates current premises of language in science education (e.g., as problem versus resource) within epistemological practices shaping the field's…

As distinct communities of practice (COP), science education research (SER) and environmental education research (EER) have both matured a great deal in recent decades, coming to include a greater diversity of theoretical perspectives, worldviews, and researcher and participant voices. In this paper, we present a view of theoretical inclusivity…

This study pursued two major objectives. The first was to use bibliometric techniques to examine bidirectionality in the relationship between teachers and researchers, as indicated by collaborative authorship among these communities. The second was to explore more deeply knowledge mobilization to classrooms by documenting the extent to which…