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Information and communication technology (ICT)‐enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta‐cognition, strategy and affect. The automated detection of these constructs allows EDM researchers to develop intervention strategies that can be implemented either by the software or the teacher. It also allows for secondary analyses of the construct, where the detectors are applied to a data set that is much larger than one that could be analyzed by more traditional methods. However, in many cases, the data used to develop EDM models are collected from students who may not be representative of the broader populations who are likely to use ICT. In order to use EDM models (automated detectors) with new …