This is an NSF funded project called May’s Journey. This a narrative platformer game designed to teach novice programmers how to program in a fun and playful way. The player confronts a character named May who asks for their help as the game world is falling apart and the code of the game needs to be pieced together in order for the characters in the game to survive. Through the game, the player pieces parts together through programming in a language designed by the game designers similar in syntax to Java. The player learns object oriented programming, programming constructs such as loops, conditionals, etc. but also good programming practice such as abstraction, modularity, and strategic debugging. See project website for publications and to play the game. NSF Award #1810972.
People:
PI: Magy Seif El-Nasr. Co-PI: Elizabeth Rowe (TERC)
Lead Researcher: Chaima Jemmali (PhD candidate at Northeastern University working under Dr. Seif El-Nasr’s supervision).
Researchers: Erica Kleinman, Carter Ithier, Mia Almeida, Kimin Lee, and Noah Young
Part Collaborators: Alex Ma, Robert Liebergen, Sanjana Dutt, Rahul Ravindran, and Nikitha Preetham.
This is an NSF funded project called Open Player Modeling. The project looks at integrating reflection mechanics into games for learning. In particular, we chose to integrate reflection mechanics into a game called Parallel developed by Drexel to teach Parallel programming, a very difficult subject to cognitively capture. To center on reflection we use novel visualizations and modeling techniques that create abstractions of players’ data and other students’ data thus emphasizing learning. The visualization take inspiration from our previous visualization system called Glyph, see our publication on Glyph’s use to infer strategies on ARXIV. See the project website through the link above for publications and link to the game. NSF Award #1917982.
People:
PI: Magy Seif El-Nasr. Co-PIs: Brian Smith (Boston College) and Jichen Zhu (Drexel)
Researchers: Jennifer Villareale, Erica Kleinman, Paola Rizzo, Varun Sriram, Zhaqing Teng, Andy Bryant, Thomas Fox, and Colan Biemer.
This is a project in collaboration with Charles River Analytics funded under the Office of Naval Research. The project explores the use of probabilistic programming languages to create an inverse behavior system, that will generate probabilistic behavior trees of what may have occurred including goals and higher level behaviors the system thinks a person is doing given their raw data. The current system explores the data from Dota2 with trees generated as shown in the screenshot. The vision is to use visualization and a visual interface to allow for intervention by a human to correct the learned behaviors in an effort to enhance the plan recognition engine.
People:
PI: Magy Seif El-Nasr in collaboration with Spencer Lynn and Bryan Loyal at CRA.
Researchers: Erica Kleinman, Nikitha Preetham, Zhaqing Teng, and Andy Bryant.