- Computational Ideation in Scientific Discovery: Interactive Construction, Evaluation, and Revision of Conceptual Models
- Attitudinal Gains from Engagement with Metacognitive Tutors in an Exploratory Learning Environment
- MILA–S: Generation of agent-based simulations from conceptual models
- Learning about Representational Modality: Design and Programming Projects for Knowledge-Based AI
- Metacognitive Tutoring for Scientific Modeling
OMS CS7637: Knowledge-Based AI: Cognitive Systems
The most important principle for designing lively eLearning is to see eLearning design not as information design but as designing an experience. –Cathy Moore
Read more about OMS CS7637: Knowledge-Based AI: Cognitive Systems
Beginning in February of 2014, I began co-developing a course for Udacity and the Georgia Tech Online Master's of Science in Computer Science with my thesis adviser, Ashok Goel. The course, Knowledge-Based AI: Cognitive Systems, is organized around three primary learning goals. First, this class teaches the concepts, methods, and prominent issues in knowledge-based artificial intelligence. Second, it teaches the specific skills and abilities needed to apply those concepts to the design of knowledge-based AI agents. Third, it teaches the relationship between knowledge-based artificial intelligence and the study of human cognition. After completing development of the course, I transitioned into co-instructing the course in Fall 2014.
From the beginning, my involvement in developing this course was intended as an exploration of and experiment in online learning. How do we leverage what we know from the learning sciences in an online course? What are the biggest obstacles to creating an engaging and effective learning experience online? And, perhaps most interestingly to me, what are the opportunities in online education to not only replicate traditional educational experiences, but improve them? What can we do online that we have difficulty doing in person?
Toward this end, we use this online course to explore several issues in online education. First, I have developed a collection of nanotutors -- AI agents that tutor small, specific skills -- that are deployed directly in the context of interactive exercises and activities within the lessons of the class to provide individualized, embedded feedback to students. Second, we have leveraged peer-to-peer feedback in order to explore scaling a rapid feedback cycle up to a large number of students. Third, we have explored project-based learning in the context of an online classroom as a way of providing engaging, personalizable, and measurable assessments. Fourth, we have investigated key issues in online learning, such as ways to overcome the feeling of isolation in an online class, the specific opportunities provided by online discussions, and the unique demographics, interests, and priorities of online students.
For more information on my experience with the Georgia Tech OMS generally and the Knowledge-Based AI class more specifically, take a look at my blog.
MILA: Modeling & Inquiry Learning Application
Science is more than a body of knowledge. It is a way of thinking; a way of skeptically interrogating the universe with a fine understanding of human fallibility. –Carl Sagan
Read more about MILA: the Modeling & Inquiry Learning Application
For my dissertation research, I designed, implemented, deployed, and evaluated a collection of tools teaching an authentic process of scientific modeling and inquiry to middle school students. The suite of tools consists of three parts: an exploratory learning environment (MILA, Modeling & Inquiry Learning Application), a metacognitive tutoring system (MILA–Tutoring), and a simulation compilation agent (MILA–Simulation).
MILA is an exploratory learning environment implemented in Java. In MILA, students describe an ecological phenomenon, propose hypotheses for what might cause that phenomenon to occur, construct explanatory conceptual models for how that hypothesis might explain that phenomenon, and provide evidence in support of their explanation. Toward this end, MILA also allows students to participate in a variety of inquiry activities, like gathering data, experimenting with simulations, and exploring related systems.
Within MILA, my main research has been on a metacognitive tutoring system, MILA–T. MILA–T is comprised of five intelligent agents that monitor student behavior within MILA and provide mentorship, guidance, critiques, and support within the modeling and inquiry activity. MILA–T was tested in a controlled study with over 200 students, and analysis showed that engagement with MILA–T improved students' attitudes towards science, and also improved the explanations of ecological phenomena that students produced.
MILA, MILA–T, and MILA–S have spawned multiple ongoing collaborations striving to support the entire life cycle of a scientist, from secondary school to college to amateur to professional. Upcoming proposed work with the Georgia State University College of Education aims to scale MILA and MILA–T into a more comprehensive curriculum for inquiry-driven modeling in middle school science. Upcoming work with the Georgia Tech School of Biology explores how the same principles and tools can support college-level students. Proposed work with the Smithsonian Institute aims to connect MILA with amateur and professional scientists.
On August 19th, 2014, Prof. Ashok Goel and I launched CS7637: Knowledge-Based AI: Cognitive Systems as part of the Georgia Tech Online Master's in Computer Science. To the left is the first video in the course, an introduction where we introduce ourselves and our course to the students in the class. To hear more about our experiences in the OMS, watch our Brown Bag talk or check out my blog.
On December 4th, 2014, Prof. Ashok Goel and I delivered a 40-minute talk to the GVU Brown Bag at Georgia Tech. The talk, titled "Putting Online Learning and Learning Sciences Together", covered our experience with trying to bring a learning sciences perspective to the development of an online course. To the right are a handful of pictures from the talk. To view the talk in its entirety, please visit talk's page on the GVU Brown Bag Archive.
- Ph.D. in Human-Centered Computing ♦ Georgia Tech ♦ Specialized in Learning Sciences & Technology ♦ Thesis titled "Metacognitive Tutoring for Inquiry-Driven Modeling" ♦ 2009-2015
- M.S. in Human-Computer Interaction ♦ Georgia Tech ♦ Specialized in Learning Sciences & Computing Education ♦ 2008 - 2009
- B.S. in Computer Science with High Honor ♦ Georgia Tech ♦ Certificate in Social & Personality Psychology ♦ Specializing in People & Media ♦ 2005 - 2008
- Udacity ♦ Mountain View, California ♦ Course Developer & General Course Manager ♦ February 2014 - Present
- Georgia Tech ♦ Atlanta, Georgia ♦ Graduate Research Assistant ♦ August 2009 - January 2014
- QOil ♦ Atlanta, Georgia ♦ User Interface Designer ♦ June 2008 - December 2008
- Media Technology Solutions ♦ Norcross, Georgia ♦ User Interface Designer ♦ May 2006 - May 2008
- Self-Employed ♦ Atlanta, Georgia ♦ Private Tutor ♦ August 2003 - Present
- Georgia Tech ♦ August 2009 – December 2014 ♦ "Fostering Complex Systems Learning in Early Science Education" ♦ Supervised by Principal Investigator Dr. Ashok Goel.
- Georgia Tech ♦ August 2008 – May 2009 ♦ "A Language-Neutral Assessment for Introductory CS Knowledge" ♦ Supervised by Investigators Allison Tew and Dr. Mark Guzdial.
- Georgia Tech ♦ January 2008 – April 2008 ♦ "Investigating the Effects of Variously Moded Educational Materials on Learning" ♦ Supervised by Principal Investigator Dr. Jim Foley.
If parents want to give their children a gift, the best thing they can do is to teach their children to love challenges, be intrigued by mistakes, enjoy effort, and keep on learning. –Carol Dweck, Mindset: The New Psychology of Success