Generalizing automated detection of the robustness of student learning in an intelligent tutor for genetics.

Recently, there has been growing emphasis on supporting robust learning within intelligent tutoring systems, assessed by measures such as transfer to related skills, preparation for future learning, and longer term retention. It has been shown that different pedagogical strategies promote robust learning to different degrees. However, the student modeling methods embedded within intelligent tutoring systems remain focused on assessing basic skill learning rather than robust learning. Recent work has proposed models, developed using educational data mining, that infer whether students are acquiring learning that transfers to related skills, and prepares the student for future learning (PFL). In this earlier work, evidence was presented that these models achieve superior prediction of robust learning to what can be achieved by traditional methods for student modeling. However, using these models to drive intervention by educational software depends on evidence that these models remain effective within new populations. To this end, we analyze the degree to which these detectors remain accurate for an entirely new population of high school students. We find limited evidence of degradation for transfer. More degradation is seen for PFL. This degradation appears to occur in part because it is generally more difficult to infer this construct within the new population.