Assessing Instructional Modalities: Individualized Treatment Effects for Personalized Learning

ABSTRACT

Estimating the efficacy of different instructional modalities, techniques, and interventions is challenging because teaching style covaries with instructor, and the typical student only takes a course once. We introduce the individualized treatment effect (ITE) from analyses of personalized medicine as a means to quantify individual student performance under different instructional modalities or intervention strategies, despite the fact that each student may experience only one “treatment.” The ITE is presented within an ensemble machine learning approach to evaluate student performance, identify factors indicative of student success, and estimate persistence. A key element is the use of a priori student information from institutional records. The methods are motivated and illustrated by a comparison of online and standard face-to-face offerings of an upper division applied statistics course that is a curriculum bottleneck at San Diego State University. The ITE allows us to characterize students that benefit from either the online or the traditional offerings. We find that students in the online class performed at least as well as the traditional lecture class on a common final exam. We discuss the general implications of this analytics framework for assessing pedagogical innovations and intervention strategies, identifying and characterizing at-risk students, and optimizing the individualized student learning environment.

KEYWORDS:

• Educational data mining
• Lasso
• Learning analytics
• Persistence
• Random forest
• Traditional lecture classroom

Additional information

Funding

This research was supported in part by NSF grant 1633130. Josh Beemer was supported by an NSF S-STEM fellowship and as a graduate research assistant in the SDSU office of Analytics Studies and Institutional Research and office of Instructional Technology Services.