The Future of Learning Lab is at the forefront of research into efficacy and equity in digital learning. We collaborate with education providers around the world to improve our understanding of how technology can advance human development and how the affordances and design of technology influences the distribution of learning benefits. All projects pursue a common goal of developing and testing scalable approaches to achieve robust learning and career outcomes for diverse learners.
The lab is currently advancing the following three programs of research:
- Achievement, behavior, and motivation in scalable learning environments
- Targeted interventions to reduce participation and achievement gaps by providing supportive learning environments
- Student choice and information ecosystems in higher education
Achievement, behavior, and motivation in scalable learning environments
Scalable learning environments include everything from online degree/certificate programs, for-credit online courses, massive open online courses, to SMS-based mobile learning. Today, the scalability of learning environment for benefits of increased access and reduced costs also poses challenges for learner retention and achievement. Moreover, as the population of online learners is growing in number, so is its diversity. We have investigated the interplay between motivation, behavior, and achievement in numerous learning environments. This research is helping advance our scientific understanding of how technology and psychological processes interact to support learning and development. Across several projects, we investigate temporal patterns of learning, self-regulated learning behaviors, socio-demographic participation and achievement gaps, and their relationship with psychological factors like belonging, social identity threat, intelligence mindsets, and learning intentions. See our related publications.
Targeted interventions to reduce participation and achievement gaps
Targeted and timely interventions can enhance motivation, sustained engagement, and robust learning outcomes. They can also reduce gaps in participation and achievement by helping create a supportive learning environment. Digital learning environments generate large quantities of data that enable us to detect, for instance, when learners are struggling; but unless we develop a causal understanding to decide which intervention to provide at what time, the systems will fail to help these learners. We are collaborating with Arizona State University, Dartmouth, Harvard, MIT, Stanford and multiple learning platforms to conduct large-scale randomized field experiments with the goal of identifying who different interventions are working for (i.e. treatment heterogeneity), under what conditions they work (i.e. contextual moderators), and intermediate outcome measures that predict final outcomes to facilitate fast iteration (i.e. surrogate outcomes). This research aims to extend equal opportunities to diverse learners by creating more inclusive technologies. Psychologically inclusive design is an approach to creating digital environments that afford equal opportunities to diverse learners by strategically manipulating content and design cues that influence how they perceive the environment. The digital format offers unique affordances to adapt visual/verbal content and visual/interaction design to this end. The lab is at the forefront testing the behavioral consequences of e.g. images, statements, and videos intended to improve social belonging among female students in STEM classes. See related publications.
Student choice and information ecosystems in higher education
Courses are the building blocks of a college degree program and there are plenty to choose from at any given institution of higher education. Yet most students are aware of only a small fraction of all the courses available to them. The process of exploring courses is fateful because it shapes subsequent choices of a major and even career path. In collaboration with Stanford’s Carta Lab, we are investigating the process of course and major choice, the role of information systems in the process, and how to select and visualize information to encourage constructive deliberation. To this end, the lab is developing and instrumenting course information systems for the dual purpose of supporting students and conducting research. There are four ongoing projects: first, assessing the effects of a course information platform on enrollment and the process of course consideration in collaboration with Cornell’s College of Human Ecology; second, we developed CourseCrafter, a search engine for courses that encourages interest-driven exploration, to understand search behavior and expressions of interest; third, we developed Pathway, a visualization of course pathways to particular majors to study forward-thinking consideration processes and ways to convey the fateful nature of initial course choices; fourth, we working together with the Carta Lab, we study the properties of the funnel of choice and ways of nudging students to consider courses outside of their major. See related publications.