Data-driven Intelligent Tutoring Systems

Deep Thought

Deep Thought is an intelligent tutor for the practice of solving deductive logic proof problems in graphical representation; displaying the proofs as logical premises, with buttons for logical rules that can be applied to selected premises and derived expressions, and a logical conclusion as the goal of the problem. We have been incrementally augmenting the Deep Thought logic tutor with data-driven methods for formative feedback and hint generation, problem selection, and worked examples to improve student learning of logic proof solving and reduce tutor dropout. Our long term goal is an intelligent tutor for logic proof construction that is fully data-driven, and can adapt to students learning logic with varying curricular requirements without the need for further expert input.

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iSnap is an extension of Snap - a block-based programming environment for novices - which adds intelligent features such as detailed logging, data-driven hints derived from past students, and support for subgoals. The goal of iSnap is to support novices learning to solve creative, open-ended programming problems.

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Educational Games


BOTS is a programming puzzle game designed to teach fundamental concepts of coding to novice programmers. In the game, players write code in a drag-and-drop programming language. The object of the game is to move a robot around mazes, solving puzzles and clearing obstacles. Our research focus is on how player-generated content can be used in educational games. With BOTS, we are testing several different designs for level creation tools to see which results in the highest quality levels and the best experience for the creators.

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Created by MIND Research Institute, ST Math is game-based instructional software for K-12 and is designed to boost math comprehension and proficiency through visual learning. Current research, in collaboration with Dr. Rutherford and Dr. Lynch, focuses on applying data-driven methods to identify opportunities for platform improvement and actionable recommendations.

More Information: MIND Research Institute


Snag'em is a large group networking game that is essentially a human scavenger hunt. Snag'em is a web-based game using that allows players to create their online profile and forge connections with other users. In this social networking game, players create a list of facts about themselves, called tags, which are then randomly presented to other players as missions. Missions are presented in the format,SNAG someone who works in the Game2Learn lab, and can only be completed by interacting with a person who fit this qualification. This game is particularly effective for fostering connections between first time conference goers and undergrads.

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Emigre is a prototype game to teach the history of immigration to the United States. The game currently follows Hind, and Lebanese immigrant at the turn of the 20th century, making here way from Mount Lebanon, through Beirut, Marseilles, and eventually Ellis Island to reach the United States. Along the way, she encounters challenges and friends, reflecting the real-life stories of similar immigrants who have made the journey. The game focuses on narrative to tell Hind's story, but weaves in resource management, exploration and random events to keep the game interesting. The game is currently in a prototype phase, and follows Hind only though the end of Beirut.

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Being is a game developed in collaboration with the Khayrallah Program for Lebanese-American Studies, which is a rhythm game teaching traditional Lebanese dances using the Microsoft Kinect. Players dance along to the music, following the recorded performance as well as graphical cues, and are scored based on how accurately they follow the dance.

Code Breaker

CodeBreaker is a game for 6th graders to learn how to solve pre-algebraic expressions. This object of the game is to crack the code and break open the high-tech locks by solving the pre-algebraic expressions by calculating the output with the given input and expression. CodeBreaker is underdevelopment in its early stages so does not yet have a demo but will soon be available online and on tablet.

Data Mining


InVis is a tool for the analysis of student-system interaction data collected from Intelligent Tutoring Systems or other educational tools. InVis 1 provided a graphical workbench that allowed users to load existing system logs, construct interaction networks, and to identify frequently used paths and other features. InVis 2, currently under construction, will provide for the programmatic construction of interaction networks, the automatic evaluation of those networks, their export in standard formats, and the calculation of value iteration for hint extraction via the Hint Factory algorithms.


The ModSoc project is a collaborative research project focused on the identification of useful behavioral patterns in online educational tools. The project is joint work between ourselves, and researchers at the Teacher's College at Columbia University, The Educational Testing Service, and Arizona State University. As part of this work we are collecting repositories of data from blended courses offered at NCSU and UC. Berkely as well as MOOCs offered through the Teacher's College. We have already completed the analysis of meaningful student communities in this data and shown that students form at-will communities that can reflect their own subsequent performance. We are also examining the role of peer tutoring in this data as well as the paths students take through online materials.


The IUSE project is focused on the extraction of pedagogical and conceptual hints from existing tutoring technologies. This is joint work by Dr. Min Chi, and Dr. Tiffany Barnes. Dr. Chi's work has previously been focused on the extraction of pedagogical guidance from student data and she is continuing that work here to extract pedagogical guidance from the existing logic tutoring data. Dr. Barnes' work is focused on the extraction of conceptual information from new domains such as probability.

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Broadening Participation in Computing

Beauty and Joy of Computing

The Beauty and Joy of Computing (BJC) is an introductory computer science curriculum piloted for the AP CS Principles course being developed by the College Board and the National Science Foundation. Computing has changed the world in profound ways. It has opened up wonderful new ways for people to connect, design, research, play, create, and express themselves. However, just using a computer is only a small part of the picture. The real transformative and empowering experience comes when one learns how to program the computer, to translate ideas into code. This course teaches students how to do exactly that, using SNAP! (based on Scratch), one of the friendliest programming languages ever invented. But this course is far more than just learning to program. We focus on some of the "Big Ideas" of computing, such as abstraction, design, recursion, concurrency, simulations, and the limits of computation. We show some beautiful applications of computing that have changed the world, talk about the history of computing, and where it will go in the future.

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STARS Computing Corps is a community of practice for student-led regional engagement as a means to broaden participation in computing. The NCSU STARS (Students & Technology in Academia, Research and Service) Student Leadership Corps (SLC) is a student organization that provides students with the opportunity to learn more about computer science careers, participate in service and outreach programs to local schools, engage in research, meet with leaders in the computer field.

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REU (Research Experiences for Undergraduates)

The Intelligent and Interactive Media and Games REU Site at NC State University immerses a diverse group of undergraduates in a vibrant research community working on cutting-edge interactive and intelligent media including serious games and game technologies, interfaces, visual experiences, and natural language dialogue. The IIM REU at NC State recruits broadly, but focuses on providing opportunities for students from underrepresented groups, and from colleges and universities with limited research opportunities, reaching students not typically exposed to research.

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