Computing Education Research
Computing education research (CER) forges new discoveries in the design and application of teaching and learning technologies related to computing, broadly defined, with a focus on how people can benefit pedagogically from using these interventions. CER is an example of discipline-based education research, which is part of the broader field of education and learning science research.
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Michael J. Lee |
Research Areas: Human-computer interaction (HCI), computing education research (CER) Advancing STEM Education with Gaming and Virtual Reality We explore unique approaches to teaching STEM topics in formal and informal learning environments. Our work on Gidget—an online game (helpgidget.org) to teach students introductory programming concepts—is effective in attracting and engaging a broad audience, including women and underrepresented minority groups in computing. Our work with CSpresso uses an interactive VR environment to teach middle school and high school students introductory computer science concepts such as binary counting and sorting algorithms. Increasing Diversity in STEM Through Mentorship We explore how to increase participation and diversity in computing, especially for underserved and underrepresented minorities in STEM. We partner with local nonprofits and schools to provide programming experience to K-12 students, specifically using near-peer mentors to teach and engage middle school and high school students in a programming camp called Newark Kids Code. We also provide an introductory computing course for in-service high school teachers during the summer. |
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Jamie Payton |
Research Areas: Pervasive computing, mobile sensing, Human-computer interaction (HCI), computing education research (CER), broadening participation in computing (BPC) Connecting data structures and algorithms assignments to dynamic, real world data set The BRIDGES project aims to enable the creation of more engaging, socially relevant assignments in introductory computer science courses by providing students with a simplified API that allows them to populate their data structure implementations with live, real-world data sets, such as those from popular social networks and web repositories (e.g., Twitter, Facebook, IMDb). The BRIDGES software system also allows students to create and explore visualizations of each executed data structure that they implement, which can promote better understanding of data structures and underlying algorithms. Students using BRIDGES show increased learning gains and successful progression in following courses in the CS major compared to a control group. Ongoing projects explore the integration of AI into the BRIDGES software platform to support the identification of individual student knowledge gaps and generation of customized challenge problems that connect to their prior, demonstrated proficiencies. |
