Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).

Autor/inn/en	Sen, Ayon; Patel, Purav; Rau, Martina A.; Mason, Blake; Nowak, Robert; Rogers, Timothy T.; Zhu, Xiaojin
Titel	Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).
Quelle	(2018), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Visual Aids; Visual Perception; Data Analysis; Artificial Intelligence; Sequential Learning; Perceptual Development; Accuracy; Difficulty Level; STEM Education; Instructional Effectiveness + Suchen Sie Ihr Suchwort? Anschauungsmaterial; Visuelle Wahrnehmung; Auswertung; Künstliche Intelligenz; Didaktische Sequenzierung; Lernsequenz; Wahrnehmungsentwicklung; Schwierigkeitsgrad; STEM; Unterrichtserfolg
Abstract	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 fluency. Such processes are highly influenced by sequence effects. 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-fluency 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 significantly 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. [For the full proceedings, see ED593090.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01