We present a method for assessing science inquiry performance, specifically for the inquiry skill of designing and conducting experiments, using educational data mining on students' log data from online microworlds in the Inq-ITS system (Inquiry Intelligent Tutoring System; www.inq-its.org). In our approach, we use a 2-step process: First we use text replay tagging, a type of rapid protocol analysis in which categories are developed and, in turn, used to hand-score students' log data. In the second step, educational data mining is conducted using a combination of the text replay data and machine-distilled features of student interactions in order to produce an automated means of assessing the inquiry skill in question; this is referred to as a "detector." Once this detector is appropriately validated, it can be applied to students' log files for auto-assessment and, in the future, to drive scaffolding in real time. Furthermore, we present evidence that this detector developed in 1 scientific domain, phase change, can be used--with no modification or retraining--to effectively detect science inquiry skill in another scientific domain, density.