Navigated learning: An approach for differentiated classroom instruction built on learning science and data science foundations

Classroom teachers are often provided with instructional resources and assessment systems that dictate one pathway for every student's learning and evaluation. These practices remain common despite new affordances available through data‐rich, emerging digital technologies that draw on data science and learning science foundations to complement and enhance traditional instruction. This paper presents a conceptual framework for Navigated Learning, a pedagogical approach that operationalizes learning principles using emerging ideas in artificial intelligence and data science, resulting in the continuous, real‐time generation of students' cognitive and noncognitive data to support a teacher's ability to utilize the system to customize instruction. The paper articulates the learning principles underlying the pedagogical approach and the features afforded by the Learning Navigator system. The paper concludes with two cases of very different implementation of Navigated Learning focused on fifth grade and ninth grade students' learning of mathematics.