Tag Archives: Intelligent tutors and tools

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 […]

EAGER: Collaborative Research: Virtual STEM Buddies for Personalized Learning Experiences in Free Choice Informal Learning Settings

PIs: Kyle Johnsen, University of Georgia Research Foundation (Award Details) Karen Kelly, The Children’s Museum of Atlanta (Award Details) Elizabeth DiSalvo, Georgia Tech Research Corporation (Award Details) This project investigates how to provide customized instructional scaffolding to young kids to learn science in unstructured, out-of-school environments. The innovation is in using ‘virtual STEM buddies’ (VSBs), […]

EXP: Helping Teachers Help Their Students: Teachers’ Use of Intelligent Tutoring Software Analytics to Improve Student Learning.

PIs: Vincent Aleven, Bruce McLaren Carnegie-Mellon University Award Details Improving learning in K-12 Science, Technology, Engineering, and Mathematics (STEM) is a critical national need as more jobs require STEM proficiency. Recently, Intelligent Tutoring Systems (ITSs) have shown great promise in improving STEM learning in middle school and high school. While these tutors support students directly, […]

EAGER: Computational Models of Essay Rewritings

PIs: Rebecca Hwa, Diane Litman University of Pittsburgh Award Details Natural language processing (NLP) is an integral part of an intelligent tutoring system for writing; it allows the system to automatically analyze student writings and provide feedback to help students to learn. For example, methods have been developed to automatically detect and correct grammar usage […]

EXP: Partners in Learning: Building Rapport with a Virtual Peer Tutor

PIs: Justine Cassell, Louis-Philippe Morency, Amy Ogan Carnegie-Mellon University Award Details This project seeks to understand, and capitalize on, how teachers or tutors build rapport with learners by building technologies that support rapport. Research will study what rapport with learners looks like, when students deploy rapport techniques, when and how deploying rapport techniques (whether by […]