Build, Understand, & Tune Interventions that Cumulate to Real Impact

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

The University of Pittsburgh has received an NSF Improving Undergraduate STEM Education: Education and Human Resources Design and Development tier award to bring together a highly interdisciplinary team to study a suite of instructional, cognitive-skill, and social/motivational interventions that have been demonstrated to produce large improvements in learning in the context of introductory STEM courses. This research is significant because it will allow us to understand which interventions produce long-term positive outcomes, whether these interventions combine negatively or synergistically within and across courses, and the types of situations or groups of students for which they are most effective. The project team consists of four Discipline-Based Education Research scientists (biology, chemistry, physics, and psychology), three learning scientists with expertise in social/motivational, cognitive skills or active STEM learning techniques, and a learning scientist with expertise in large-scale longitudinal data analysis with cutting edge statistical techniques. This project represents the initial phase of a new type of study termed "Intervention Science." Although interventions are relatively commonplace in educational settings, very little research has sought to comprehensively evaluate different interventions in a single, interdisciplinary methodological framework. The research plan seeks to understand the relative influence of different classroom "interventions" (e.g. "flipped" classes, active learning, peer assessment, etc.) on indicators of student success (e.g. course performance in downstream courses, participation in undergraduate research, conceptual learning gains, etc.). Construction of control and experimental groups will be accomplished using statistical techniques that assign students probabilistically to such categories. Confounding circumstances (e.g. students enrolled in different courses experiencing different interventions, or students interacting with peers in other courses) will be handled using techniques such as hierarchical linear modeling. Knowledge of the conditions in which instructional, cognitive-skill, and social/motivational interventions are most effective, and on whom, will inform the way in which faculty members implement effective interventions in the classroom and, in that way, have a significant impact on the educational experience of large numbers of STEM students nationwide.