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A key goal of science education articulated in A Framework for K-12 Science Education is to create opportunities for students to answer questions about the world that connect to their interests, experiences, and identities. Interest can be seen as a malleable relationship between a person and object (such a phenomenon students might study). In this paper, we analyzed data from a design study of an online course focused on preparing 11 secondary teachers to design three-dimensional tasks that align to the Next Generation Science Standards and that connect to students’ interests. Our data sources were teachers’ descriptions of their design decisions about what phenomena to use to anchor assessment, designed assessment tasks, and interviews with them about those decisions. We found that interest was an important consideration for assessment design, but they considered student interests in different ways. Some teachers shifted their views of what it meant to engage student interests in the context of assessment design over the course of their participation in professional learning. Most teachers made decisions about what they believed their students were interested in based on their knowledge of students or beliefs about their students’ interests. In supporting teachers to design summative assessments that link to students’ interest, it is critical to assume teachers bring a range of conceptions of interest, and to consider the feasibility and utility of task design tools from teachers’ point of view. 

This work investigates how tutoring discourse interacts with students’ proximal knowledge to explain and predict students’ learning outcomes. Our work is conducted in the context of high-dosage human tutoring where 9th-grade students attended small group tutorials and individually practiced problems on an Intelligent Tutoring System (ITS). We analyzed whether tutors’ talk moves and students’ performance on the ITS predicted scores on math learning assessments. We trained Random Forest Classifiers (RFCs) to distinguish high and low assessment scores based on tutor talk moves, student’s ITS performance metrics, and their combination. A decision tree was extracted from each RFC to yield an interpretable model. We found AUCs of 0.63 for talk moves, 0.66 for ITS, and 0.77 for their combination, suggesting interactivity among the two feature sources. Specifically, the best decision tree emerged from combining the tutor talk moves that encouraged rigorous thinking and students’ ITS mastery. In essence, tutor talk that encouraged mathematical reasoning predicted achievement for students who demonstrated high mastery on the ITS, whereas tutors’ revoicing of students’ mathematical ideas and contributions was predictive for students with low ITS mastery. Implications for practice are discussed.