Product Analysis: Differential Responses to Personalized Learning Recommendations Revealed by Event-Related Analysis
Lexia Research & Analytics (email@example.com)
Kevin Dieter, Jamie Studwell and Kirk Vanacore "Differential Responses to Personalized Learning Recommendations Revealed by Event-Related Analysis" In: Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020), Anna N.~Rafferty, Jacob Whitehill, Violetta Cavalli-Sforza, and Cristobal Romero (eds.) 2020, pp. 736 - 742.
In Core5, student weekly usage time is not closely aligned with what we recommend to them (i.e., we don’t often see students with 20 minute targets using for 20 minutes and those with 50 min targets using for 50 minutes)
BUT, Core5 student weekly usage time does respond when targets change, shifting in a manner that reflects both the direction (up/down) and amount of the change in target
In addition, students who meet or exceed their usage target in a given week are more likely than those who did not to stay on track towards their end of year benchmark the next time they use the program.
Together, these results suggest that personalized usage targets are helping teachers tailor usage to student needs despite structural forces that restrict the flexibility of usage time across students (e.g. class schedules, student-driven use outside of class, challenges implementing different usage times within the same classroom, etc.)