Building Strategies for a Personalized Learning Mentor App: A Design Case

• In Event: AERA Poster Session 12
  In Poster Session: Instructional Strategies and Learning Tools to Support STEM Learning

Abstract

In this design case, we describe the inception, design process, use, and next steps for the development of a resource library and strategies for mentors to help students learn math using educational technology. This was part of a larger project in which we created an AI-based tutoring support system to help narrow achievement gaps in mathematics. For this, we partnered with four local districts, each with unique types of mentors, grade levels, and timing of sessions. Specifically, we describe how we designed and refined these strategies, highlighting our challenges and successes, as well as steps for future iterations.

Authors

• Nikki G. Lobczowski, McGill University

• J. Elizabeth Richey, Carnegie Mellon University

• Patricia Yu, Carnegie Mellon University

• Lauren Herckis, Carnegie Mellon University

• Kenny Donaldson, University of Pittsburgh

• Lee Branstetter, Carnegie Mellon University

• Kenneth R. Koedinger, Carnegie Mellon University