PIs: Fengfeng Ke, Matthew Ventura, Valerie Shute, Kathleen Clark, Gordon Erlebacher Florida State University Award Details In this Cyberlearning: Transforming Education Exploration project, the PIs investigate how to help middle schoolers simultaneously develop mathematics understanding, mathematics skill, and mathematics disposition. They are taking design-based pedagogical approach, having students practice the math they are learning through […]
Tag Archives: Computer-assisted assessments
DIP: Using Dynamic Formative Assessment Models to Enhance Learning of the Experimental Process in Biology
PI: Eli Meir, Joel Abraham, Eric Klopfer, Zhushan Li SimBiotic Software Award Details This project seeks to develop a dynamic formative assessment method for use with virtual labs. The research focuses on how to constrain a virtual lab experience to be amenable to automated feedback on relatively open-ended responses students are generating while still giving […]
DIP: Collaborative Research: Mixed-Reality Labs: Integrating Sensors and Simulations to Improve Learning
PIs: Charles Xie, Concord Consortium (Award Details) Jennifer Chiu, University of Virginia (Award Details) This collaborative project is investigating the characteristics of a mixed-reality learning environment that combines the rich context and multi-sensory experiences of a physical lab with the interactive simulations of a virtual lab. The hybrid environment integrates sensors and simulations to bring […]
DIP: Interaction Research in Complex Informal Learning Environments
PIs: Stephen Uzzo, Marc Levy, Jan Plass, Eric Siegel, Margaret Honey New York Hall of Science Award Details This project brings together an interdisciplinary team of learning science researchers, game designers, game theory researchers, and environmental scientists to engage diverse audiences in the science of sustainability and sustainable development. Taking its inspiration from Buckminster Fuller’s […]
DIP: Exploiting Longitudinal Electroencephalogram (EEG) Input in a Reading Tutor
PIs: David Mostow, Kai-Min Chang Carnegie-Mellon University Automated (and human) tutors are limited in their ability to infer what is going on in students’ heads based on their observable behavior. The proposed work addresses this limitation by investigating how EEG input from a commercially-available device can be used as evidence about students’ mental states. In […]
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