StepWise Virtual Tutor for Math Word Problems Using SRSD

During Phase I in 2022, the project team developed a prototype of a Natural Language Processing and Machine Learning model that provides instantaneous feedback to students as they solve math word problems, including hints prior to a student setting up a word problem.  At the end of Phase I, a pilot study, with four educators and 34 high school students, demonstrated that the prototype functioned as planned, that students indicated they received just-in-time hints and supports while completing word problems, and educators stated that the information generated by the dashboard was useful and appropriate for providing individualized instruction for each student.

In Phase II of the project, the team will fully develop the product, including adding a voice input as alternative to text entry based, by continuing to add data to optimize the Natural Language Processing and Machine Learning models that setup problems and provides hints using the specific language of the word problem, fully developing the user interface for students and educators, and developing the reporting dashboard framework for web-hosted reports. After development concludes, a pilot study will test the feasibility and usability, fidelity of implementation, and the promise of the product for improving science learning. The team will collect data from 60 grade 4 and 5 classes with approximately 15 students per class, with half randomly assigned to use the product and the other half to use business-as-usual activities for the same course content.  Researchers will compare pre-and-post scores using the Stanford Achievement Test and the Mathematics Problem Solving assessment at pre-test, post-test, and follow-up. Researchers will gather cost information using the "ingredients method" and will include all expenditures on things such as personnel, facilities, equipment, materials, and training.