Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems
This work addresses improving educational technology effectiveness for middle-school students, showing incremental progress by applying a known method to new contexts.
The study tested a data-driven redesign process on four middle-school math intelligent tutoring system units, finding that while learning gains were similar, the redesigned system led to more productive time-on-task, skills practiced, and total knowledge mastery among 123 students.
Past research has defined a general process for the data-driven redesign of educational technologies and has shown that in carefully-selected instances, this process can help make systems more effective. In the current work, we test the generality of the approach by applying it to four units of a middle-school mathematics intelligent tutoring system that were selected not based on suitability for redesign, as in previous work, but on topic. We tested whether the redesigned system was more effective than the original in a classroom study with 123 students. Although the learning gains did not differ between the conditions, students who used the Redesigned Tutor had more productive time-on-task, a larger number of skills practiced, and greater total knowledge mastery. The findings highlight the promise of data-driven redesign even when applied to instructional units *not* selected as likely to yield improvement, as evidence of the generality and wide applicability of the method.