{"pk":27888,"title":"For Teaching Perceptual Fluency, Mahines Beat Human Experts","subtitle":null,"abstract":"In STEM domains, students are expected to acquire domainknowledge from visual representations that they may not yetbe able to interpret. Such learning requires perceptual flu-ency, or the ability to intuitively and rapidly see the underlyingconcepts in visuals and to translate between them. Perceptualfluency is acquired via nonverbal, implicit learning processes.Thus far, we have lacked a principled approach for identify-ing a sequence of perceptual fluency problems that promoterobust learning. Here, we describe how a novel machine learn-ing technique can generate an optimal sequence of perceptualfluency problems. In a human experiment, we show that amachine-generated sequence outperforms both a random se-quence and a sequence generated by a human domain expert.Interestingly, the machine-generated sequence resulted in sig-nificantly lower accuracy during training, but higher posttestaccuracy. This suggests that the machine-generated sequenceinduced desirable difficulties. To our knowledge, our study isthe first to show that machine learning can yield desirable dif-ficulties for perceptual learning","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"visuals"},{"word":"perceptual fluency"},{"word":"implicit learning"},{"word":"desirable difficulties"},{"word":"machine learning"},{"word":"machine teaching"},{"word":"chemistry optimal training"},{"word":"sequence effects"}],"section":"Publication-based-Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6p2256bg","frozenauthors":[{"first_name":"Ayon","middle_name":"","last_name":"Sen","name_suffix":"","institution":"University of Wisconsin-Madison","department":""},{"first_name":"Purav","middle_name":"","last_name":"Patel","name_suffix":"","institution":"University of Wisconsin-Madison","department":""},{"first_name":"Martina","middle_name":"A","last_name":"Rau","name_suffix":"","institution":"University of Wisconsin-Madison","department":""},{"first_name":"Blake","middle_name":"","last_name":"Mason","name_suffix":"","institution":"University of Wisconsin-Madison","department":""},{"first_name":"Robert","middle_name":"","last_name":"Nowak","name_suffix":"","institution":"University of Wisconsin-Madison","department":""},{"first_name":"Timothy","middle_name":"T","last_name":"Rogers","name_suffix":"","institution":"University of Wisconsin-Madison","department":""},{"first_name":"Xiaojin","middle_name":"","last_name":"Zhu","name_suffix":"","institution":"University of Wisconsin-Madison","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-02T02:00:00+08:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27888/galley/17526/download/"}]}