We are currently pursuing several interrelated lines of research:

Retrieval Practice / Test-Enhanced Learning

People generally conceptualize testing as an assessment tool for evaluating learning (summative assessment) and providing feedback to guide future learning activities (formative assessment). However, a growing body of evidence indicates that testing (i.e. retrieval practice) can also be used as a learning tool. This line of research focuses on how retrieval practice can be used to increase long-term retention and improve understanding, and the underlying mechanisms that produce these effects.

The Acquisition and Correction of False Knowledge

Our world abounds with false information – urban legends, political slander, and untruths about other cultures are just a few of the many possible examples. Often this false information is innocuous, but some of it is quite malicious because it undermines people’s understanding of the world and conflicts with learning the truth. This line of research focuses on understanding how people acquire false knowledge, especially when they have prior knowledge that contradicts the false information, and how false knowledge can be corrected.

Mnemonic Effects of Retrieval on Autobiographical Memories

Autobiographical memory plays a central role in people’s construction of a self-concept and the way in which they interact with the world around them. One basic question that has produced a large corpus of research is how the content and phenomenological characteristics of autobiographical memories change over time. This line of research focuses on how repeated retrieval (i.e. rehearsal) of autobiographical memories affects the way in which people remember events from their life.

Applying Cognitive Science to Educational Technology

The most effective educational interventions often face significant barriers to widespread implementation because they are highly specific, resource intense, and/or comprehensive. In contrast, the synergy of cognitive science, machine learning, and technology has the potential to produce inexpensive, but powerful learning tools that generalize, scale, and can be easily implemented worldwide. This line of research focuses on how advances in technology and ideas from machine learning can increase the effectiveness and impact of principles of learning from cognitive science. One large goal is to build an online personalized learning system (OpenStax Tutor) that assesses what each student knows and how that student learns best, then recommends activities that will optimize learning.