Past Research Projects

LEARNING WITH CONTRASTING EXAMPLES
Near examples copyThis work is comprised of three separate projects with collaborators in psychology, chemistry, and human-computer interaction departments.  The work explores the design of instruction around contrasting examples with an eye towards developing technology-based instruction around contrasts.  This research is being conducted with elementary and adult students learning experimental design, chemistry, and visual design. [Collaborators: David Klahr, David Yaron, and Steven Dow, Carnegie Mellon University.]

CHOICE-BASED ASSESSMENT
The goal of this project is to create a novel technology-based learning environment, predicated on the idea that students’ learning choices are predictive of their learning outcomes.  Design work focused on building an interactive, adaptive environment that would respond to student actions, encouraging them to select “good” while avoiding “bad” choices.  Research aims to determine whether this choice-adaptive environment is effective in shaping student behaviors and enhancing learning. [PIs: Daniel Schwartz, Stanford University and Gautam Biswas, Vanderbilt University.]

INVENTION ACTIVITIES
Invention activities invite students to generate an external representation of an underlying principle that exists across many examples of a phenomenon.  Example cases are designed for optimal contrasts to encourage perception of the principle and flexible understanding that is easily applied to novel problems.  Studies focus on the unique perceptual and transfer benefits of learning with invention as compared to more conventional instructional methods, like tell-and-practice. [Collaborators: Daniel Schwartz, Doris Chin, Stanford University.]

TEACHABLE AGENTS
TeachableAgent The Teachable Agent is a software learning environment in which students learn by teaching a computer character.  Studies focus on the unique learning and motivational benefits of using the Teachable Agent system, the social aspects of learning, and the potential added value of Teachable Agents for standard classroom practice. [PIs: Daniel Schwartz, Stanford University, Gautam Biswas, Vanderbilt University.]