Use of Machine Learning to Adaptively Select Activity Types and Enhance Student Learning with an Intelligent Tutoring System

The research team will carry out activities designed to answer three research questions: (1) Does an ITS support more robust learning when activities are added to support fundamental learning mechanisms more evenly? (2) Does using a sequential decision-theoretic approach to adaptively sequence activities to the individual improve robust learning outcomes compared to a fixed sequence? and (3) Does using a self-optimizing action selection system, which uses accumulating student interaction data to improve its selection mechanism, lead to higher robust learning gains of students who use the system later on compared to students that use the initial system?

To answer these questions, the researchers will carry out three lines of research. First, they will develop new activity types for the Fractions Tutor. These activity types will be developed so that they promote sense-making and fluency-building processes. Next, they will gather initial data in classrooms using the Fractions Tutor in order to inform the development of an adaptive policy that selects among multiple activity types. Lastly, the research team will carry out two experiments. In Experiment 1, they will compare the standard tutor to two new versions of the ITS to evaluate whether robust learning is enhanced in a tutor that (unlike the standard tutor) supports multiple activity types or supports multiple activity types sequenced adaptively by the individual. This is a between-subjects design, where students will be randomly assigned to one of the three conditions. In Experiment 2, the research team will test the value of a self-improving version of the activity selection policy that leverages data gained as the tutor interacts with more students. Data will be conducted serially by classroom in five “waves.” Each wave will be considered a condition and analyses will look for differences between waves.