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During the first year of the Interactive Learning and Collaboration Environment (InterLACE) Project, we designed a Web-based technological tool in concert with high school physics and engineering teachers for use in their classrooms and engaged them in professional development activities that centered on design-based inquiry instruction to help them maximize the success of that tool when it debuted in the spring of 2012. Called the Thought Cloud, the tool allows teachers to upload questions that students can view and answer through any Internet-connected device (desktop, laptop, tablet, etc.); the students’ posts are then displayed on a centrally located screen to promote discussion and collective sense-making and to serve as a virtual public work space. Since words constitute a large share of the data that the Thought Cloud collects, we felt that lexicometry, a relatively new form of statistical textual analysis, would be a good way to examine just how our teachers used the tool. Using a lexicometric software program, we conducted an exploration of the respective vocabularies, or lexica, that each of our teachers employed to construct their questions in order to see what patterns emerged within the aggregate of these lexica, or corpus, and to determine what factors might have shaped these patterns and if they indicate a teacher’s particular instructional stance—be it a traditional lecture style, an inquiry-based approach, or something in between. In doing so, we have defined three lexical categories—content-centric, process-oriented, and student-centric—and found that the teacher who used the tool the most tended to employ words that were student-centric, or focused on evoking student reasoning, and those who used the tool least favored words that were contentcentric, or intended to merely transmit information. A closer examination of the corpus revealed that this link between lack of use and content-centric lexica does not necessarily indicate an aversion to inquiry instruction but rather the pedagogical goals the teachers had set when using the Thought Cloud in their classrooms. These results have provided us with valuable insight into the instructional stances teachers take when using our tool; therefore, we believe that incorporating lexicometry into future analyses can serve as a sort of diagnostic metric that we can use to inform our professional development activities in the coming years.

Instructional Stance as Indicated by Words: A Lexicometrical Analysis of Teacher Usage of an Inquiry-Based Technological Tool in High School Classrooms