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Orchestrating inquiry‐based science learning in the classroom is a complex undertaking. It requires fitting the culture of the classroom with the teacher's teaching and inquiry practices. To understand the interactions between these variables in relation to student learning, we conducted an investigation in two different classroom settings to understand how different teachers use and appropriate the same physical and computer‐based tools into their teaching practices. Each worked with physical aquaria, function‐oriented hypermedia for background information and reference, and NetLogo (Evanston, IL) simulations for computer‐supported collaborative inquiry learning. The students engaged in inquiry as they used the NetLogo models in small groups. Because of distinct teaching styles and varying levels of comfort with the materials and content, these two enactments were extraordinarily different between the two classrooms. We present a contrasting case analysis to examine how each teacher's practices set the stage for the kinds of interactions that occurred during students' computer‐supported inquiry learning. We suggest that one teacher worked from a cognitive‐elaboration perspective whereas the other teacher took an approach to teaching that incorporated socio‐cultural perspectives. Both of the approaches to teaching supported the active engagement of learners and may account for the similar learning outcomes measured. In light of the current move toward standards‐based public education, it may seem that there is only one pathway for teaching to support of student learning. However, our results do not support this notion and we provide evidence that different instructional models, classroom norms, and appropriation of tools can support similar student learning outcomes with respect to content knowledge. © 2014 Wiley Periodicals, Inc. J Res Sci Teach 52: 6–35, 2015

We present a novel situational task that integrates collaborative problem solving behavior with testing in a science domain. Participants engage in discourse, which is used to evaluate their collaborative skills. We present initial experiments for automatic classification of such discourse, using a novel classification schema. Considerable accuracy is achieved with just lexical features. A speech-act classifier, trained on out-of-domain data, can also be helpful.