Using computational methods to discover student science conceptions in interview data

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.