Science

This study explores how teachers across the United States answer and reason on “teacher assessment tasks” designed to elicit content knowledge for teaching (CKT) about matter and its interactions. It leverages a think aloud approach, where participants were asked to reason about different assessment items that described teaching scenarios related to elementary science instruction about topics such as properties of matter, changes in matter, the particulate model of matter, and conservation of matter. Specifically, this study investigates: (1) To what extent do the participants use the intended knowledge and reasoning when responding to items designed to assess their CKT about matter and its interactions? and (2) When participants struggle to respond accurately to these items, what were their reasons for doing so? In particular, we were interested in examining the patterns in the nature of the knowledge they fail to leverage when responding to these CKT matter items. These presentation slides were prepared for the 2020 Annual International NARST Conference.

In this paper we describe a qualitative study in which we examine individual student engagement during implementation of an instructional scaffold for critical evaluation of scientific models during Earth and space science lessons. We coded dialogic interactions of one student group in a sixth grade science classroom across three observations, wherein we analyzed the trajectory of engagement for a single student - Ray (a pseudonym), within the co-constructed learning of the group. The first of these observations involved implementation of a preconstructed scaffold, called the Model-Evidence Link (MEL) diagram, on the topic of hydraulic fracturing (fracking). With the MEL, students use evidence to compare a scientific model to an alternative model. In the second two observations, students used a more agentic variation of the activity called the build-a-MEL, to study the topics of fossils and freshwater resources respectively. After three observations, we transcribed and coded each interaction of students in the group. We then categorized and identified emerging patterns of Ray’s discourse and interactions with group members by using both a priori engagement codes and open coding. This paper was prepared for the 2020 AERA Annual Meeting.