Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems

TL;DR

This study tests a data-driven redesign process for an educational tutoring system across different topics, finding improvements in student engagement and skill practice despite no difference in learning gains.

Contribution

It demonstrates the generality of a data-driven redesign approach by applying it to various instructional units beyond those initially considered suitable.

Findings

Redesigned tutor increased students' productive time-on-task.

Students practiced more skills with the redesigned system.

Total knowledge mastery was greater with the redesign.

Abstract

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.