Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice

In STEM domains, students are expected to acquire domain knowledge from visual representations that they may not
yet be able to interpret. Such learning requires perceptual fluency: the ability to intuitively and rapidly see which concepts visuals show and to translate among multiple visuals. Instructional problems that engage students in nonverbal, implicit learning processes enhance perceptual uency. Such processes are highly in uenced by sequence eects. Thus far, we lack a principled approach for identifying a sequence of perceptual-fluency problems that promote robust learning. Here, we describe a novel educational data mining approach that uses machine learning to generate an optimal sequence of visuals for perceptual- uency problems. In a human experiment, we show that a machine-generated sequence outperforms both a random sequence and a sequence generated by a human domain expert. Interestingly, the machine-generated sequence resulted in signicantly lower accuracy
during training, but higher posttest accuracy. This suggests that the machine-generated sequence induced desirable difficulties. To our knowledge, our study is the first to show that an educational data mining approach can induce desirable difficulties for perceptual learning.