Browsing by author "Joo, Seang-Hwane"
Now showing items 1-4 of 4
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A Monte Carlo evaluation of masked visual analysis in response-guided versus fixed-criteria multiple-baseline designs
Ferron, John M.; Joo, Seang-Hwane; Levin, Joel R. (2017-09) -
An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments
Park, Jung Yeon; Joo, Seang-Hwane; Cornillie, Frederik; Van der Maas, Han; Van den Noortgate, Wim (2019) -
Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems
Pliakos, Konstantinos; Joo, Seang-Hwane; Park, Jung-Yeon; Cornillie, Frederik; Vens, Celine; Van den Noortgate, Wim (2019) -
The impact of response-guided baseline phase extensions on treatment effect estimates
Joo, Seang-Hwane; Ferron, John M.; Beretvas, S. Natasha; Moeyaert, Mariola; Van den Noortgate, Wim (2018-08)