It is widely agreed that science instruction should help students build new knowledge on the foundation of their prior knowledge. Responsive teaching refers to a family of teaching strategies that pursue and build on student ideas. We introduce a particular approach to responsive teaching and examine how it can be supported by the use of computational models. We analyse an 8th grade science teacher’s facilitation of a class discussion near the end of a lesson on sound. We present a moment-by-moment characterisation of her responsive teaching moves, highlighting the ways she used a computational model to help students articulate and examine their thinking. Our findings make empirical contributions to literature concerned with responsive teaching and literature concerned with the role of computational models in constructivist approaches to instruction.

A large body of research in the learning sciences has focused on students' commonsense science knowledge---the everyday knowledge of the natural world that is gained outside of formal instruction. Although researchers studying commonsense science have employed a variety of methods, one-on-one clinical interviews have played a unique and central role. The data that result from these interviews take the form of video recordings, which in turn are often compiled into written transcripts, and coded by human analysts. In my team's work on learning analytics, we draw on this same type of data, but we attempt to automate its analysis. In this paper, I describe the success we have had using extremely simple methods from computational linguistics---methods that are based on rudimentary vector space models and simple clustering algorithms. These automated analyses are employed in an exploratory mode, as a way to discover student conceptions in the data. The aims of this paper are primarily methodological in nature; I will attempt to show that it is possible to use techniques from computational linguistics to analyze data from commonsense science interviews. As a test bed, I draw on transcripts of a corpus of interviews in which 54 middle school students were asked to explain the seasons.