Being able to accurately assess and manage one’s own learning is essential for academic success and lifelong learning. Yet research shows that learners often hold misconceptions about which strategies are most effective (Bjork, Dunlosky, & Kornell, 2013). Our work aims to address this challenge by (1) identifying effective learning and metacognitive strategies, (2) developing interventions to support their use, and (3) uncovering the cognitive mechanisms that make them work across diverse learner populations. To pursue these goals, we use a combination of controlled lab experiments, classroom-based studies, and large-scale analyses of educational data—providing a rich, multidimensional view of how students learn and how we can help them learn better.
Uncovering the Cognitive Foundations of Effective Learning and Assessment
We aim to identify and evaluate the theoretical mechanisms that underlie effective learning and assessment strategies. Our research explores how monitoring one’s own learning can influence memory performance—a phenomenon known as metacognitive reactivitivy—and evaluates competing explanations for these effects (Janes, Rivers, & Dunlosky, 2018; Rivers, Dunlosky, Janes, Witherby, & Tauber, 2023; Rivers, Janes, Dunlosky, Witherby, & Tauber, 2023; Rivers, Janes, & Dunlosky, 2021). We also examine how (and why) requiring students to justify their answers during multiple-choice testing can enhance both learning and metacognitive accuracy (Clark, Rivers, & Overono, 2025), and how different response formats affect the benefits of retrieval practice (Rivers, Northern, & Tauber, 2025) and prequestioning before reading (Rivers, Berdelis, Pan, & Tauber, under review).
Understanding When Tests Promote Accurate Self-Assessment
Another line of our work investigates the role of test experience in fostering accurate metacognitive judgments. We study the conditions under which testing provides reliable insights into one’s own knowledge (Rivers, Dunlosky, & Joynes, 2019) and the efficacy of various learning strategies, including context-based word generation (Storm, Hickman [Rivers], & Bjork, 2016), associative learning (Rivers & Dunlosky, 2021), pretesting (Pan & Rivers, 2023), and retrieval practice (Rivers, Dunlosky, & McLeod, 2022).
Supporting the Development of Self-Regulated Learners
We are committed to promoting self-regulated learning by investigating both internal and external factors that shape students’ learning behaviors. Our research identifies key barriers—such as misconceptions about strategy effectiveness and the perceived mental effort required to use effective strategies—that hinder students from adopting evidence-based approaches (Dunlosky, Badali, Rivers, & Rawson, 2020; Rivers, 2021). We also examine how features of educational materials, such as rubrics, influence students’ study decisions (Shumaker, Rivers, & Tauber, 2025). Across projects, we develop and test interventions designed to reduce these barriers and promote more adaptive and sustainable learning habits (e.g., Rivers, 2023).