Tag Archives: Intelligent tutors and tools

CAREER: Improving Adaptive Decision Making in Interactive Learning Environments

3/1/17-2/28/22 PIs: Min Chi North Carolina State University Award Details Interactive Learning Environments (ILEs) hold great promise for improving student performance in STEM education. While, traditionally, such systems have focused on teaching students subject matter, an equally important facet is to teach them how become better learners. The objective of this project is to develop […]

EXP: Assessing ‘Complex Epistemic Performance’ in Online Learning Environments

9/1/16-8/31/18 PIs: William Cope, ChengXiang Zhai, Duncan Ferguson, Willem Els University of Illinois at Urbana-Champaign Award Details This project will develop online software tools to assess and offer feedback to learners communicating complex scientific or technical information. “Complex epistemic performance” here refers to knowledge representations in reports or case studies which involve not only facts […]

EXP: Inq-Blotter – A Real Time Alerting Tool to Transform Teachers’ Assessment of Science Inquiry Practices

9/1/16-8/31/18 PIs: Janice Gobert, Michael Sao Pedro Rutgers University Award Details This EXP project addresses the need for real-time diagnostic tools for teachers that can assess students’ needs, i.e. provide formative assessment, in order to improve science instruction. The project will extend, pilot, implement, and study Inq-Blotter, a scalable, web-based alerting system that enables teachers’ […]

DIP: Graphical Model Construction by System Decomposition: Increasing the Utility of Algebra Story Problem Solving

9/1/16-8/31/19 PIs: Kurt VanLehn, Jon Wetzel, Fabio Augusto Milner Arizona State University Award Details This project studies a new genre of learning technology that may remove a notorious bottleneck in STEM education: mathematical model construction. These days, computers can solve complex mathematical problems, but humans must still define the problem for the computer, which is […]

EXP: Exploratory Study on the Adaptive Online Course and its Implication on Synergetic Competency

9/1/16-8/31/18 PIs: Noboru Matsuda, Norman Bier, Larry Johnson Texas A&M University Award Details Most online courseware helps teach facts and concepts, while a different type of online learning software called intelligent tutoring systems can effectively teach skills in a way that is tailored to each learner. Unfortunately, these two tools are rarely integrated because of […]

EXP: Modeling Perceptual Fluency with Visual Representations in an Intelligent Tutoring System for Undergraduate Chemistry

9/1/16-8/31/19 PIs: Martina Rau, Xiaojin Zhu, Robert Nowak University of Wisconsin-Madison Award Details Instructors often use visuals to help students learn (e.g., pie charts of fractions, or ball-and-stick models of chemical molecules) and assume that students can quickly discern relevant information (e.g., whether or not two visuals show the same chemical) once that visual representation […]

EXP: Fostering Self-Correcting Reasoning with Reflection Systems

9/1/16-8/31/19 PI: Michael Hoffmann, Richard Catrambone, Jeremy Lingle Georgia Tech Award Details This research project is exploring how to support reasoning about wicked problems. These are societal important problems that are characterized by incomplete or contradictory knowledge, have a large body of differing opinion on the problem, have a large economic burden, and are intimately […]

INT: Collaborative Research: Detecting, Predicting and Remediating Student Affect and Grit Using Computer Vision

9/1/16-8/31/20 PIs: Beverly Woolf, Thomas Murray, University of Massachusetts Amherst (Award Details) Ivon Arroyo, Worcester Polytechnic Institute (Award Details) Margrit Betke, Stan Sclaroff, Boston University (Award Details) John Magee, Rutgers University New Brunswick (Award Details) This INT project integrates prior work from two well-developed NSF-sponsored projects on (i) advanced computer vision and (ii) affect detection […]

EXP: Data-Driven Support for Novice Programmers

9/1/16-8/31/19 PIs: Tiffany Barnes, Min Chi North Carolina State University Award Details The researchers in this project will study fully data-driven systems to provide both scalable and individualized support for learners. Open-ended, media-rich visual programming environments such as Scratch and Snap represent the next-generation genre for engaging and inspiring students to learn programming. However, solving […]