Predictive Modeling for High Impact Active Learning Classrooms

TL;DR

This study develops a predictive model linking active learning strategies to student learning gains in undergraduate science courses, highlighting effective activity combinations and providing actionable insights.

Contribution

It introduces a data-driven model identifying optimal active learning strategies and their impact on student conceptual understanding across multiple institutions.

Findings

Classes with specific active learning activities show large effect sizes (>2).

No-group-worksheet classes have gains similar to traditional lectures.

Recommendations for effective active learning strategies are proposed.

Abstract

Though a large body of research has shown that active learning is more effective than traditional lecture in undergraduate science courses, little research has examined which types and combinations of active learning strategies are most effective. In this study, we use a multi-field, multi-institutional dataset of 69 undergraduate science classes to create a predictive model that maps time spent on different classroom activities to student conceptual learning. We identify one type of class that produces exceptional learning gains (effect sizes > 2): 10-20% of time on group worksheets, 20-40% on group clicker questions, and two or more student questions per hour. We also find that classes without group worksheets show learning gains comparable to lecture-only courses. These results offer testable recommendations for future controlled studies.