Florence R. Sullivan, Ph.D.

Research Projects

Current Research Projects

CSforAll Springfield Research Practice Partnership

The CSforAll Springfield research-practitioner partnership (RPP) defines a four-year project that integrates standards-based computer science and computation thinking (CS/CT) concepts, learning progressions, and practices in core curricula across all Springfield Massachusetts K-5 public schools. The Partnership's primary long-term outcomes are (1) to prepare a diverse student population, including English learners, students with disabilities, underserved race and ethnicities, and students experiencing poverty, "to effectively use and create technology to solve complex problems" that they need post-high school for college and career, and (2) to grow the expertise for CS/CT teaching and learning within the District. The established CS4All_Springfield RPP of learning science, educational policy, social science and computer science researchers, experienced evaluators, teachers, school and district leaders, instructional and IT specialists, and curriculum coordinators will undertake an ambitious and innovative approach, coupling Design-Based Implementation Research, a series of agile, iterative and collaborative pilots and at-scale implementations informed by and informing research and evaluation, a strategy for using external and embedded professional development, and cross-school professional learning communities linked with current school-based PLCs.

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Girls Involved in Immersive Robotics Learning Simulations

The GIRLS project seeks to broaden the participation of girls in robotics, computer science (CS), and STEM through an innovative middle school curriculum for ubiquitous co-robotics using an adventure based online game we call Gale Force. In this game, students act as emergency managers to assist first responders and other recovery workers in responding to a natural disaster, such as a hurricane. The co-robotics team’s task is to locate and deliver supplies to victims of the natural disaster using wheeled robots and flying programmable drones. As part of this curriculum, students will discuss the social and ethical implications of ubiquitous co-robots in these settings. Our goal is to create interest among underrepresented students for CS and IT careers through increasing their awareness of how a CS or IT career can be a helping career – a career that is devoted directly to helping others. Students served in this project will learn about and model key technology innovations that help people. In so doing, students will expand their understanding of STEM and IT careers, and also begin to engage with deeper concepts in computer science, such as the role of robotics, programming, and artificial intelligence in technology innovations. The success of this strategy will be assessed through measuring changed attitudes and skillsets with respect to computer science and technology and whether students continue to be involved in weekend and vacation workshops.

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Recent Research Projects

Microgenetic Learning Analytics

The goal of the Microgenetic Learning Analytics project is to develop a new computational method for analyzing student problem-solving conversations recorded in co-present small group interactions. Microgenetic analysis seeks to understand conceptual change over short time periods (minutes, hours, days). We use tools drawn from the field of natural language processing to aid our microgenetic analysis of discourse data. Our corpus of small group talk is derived from middle school aged girls and boys working in collaborative problem solving groups to solve robotics problems. The challenge of our work is finding effective means for interpreting contextual talk that widely features indexical and pronomial elements, as opposed to a specific content-based vocabulary. Microgenetic analysis is viewed by educational researchers as one of the most robust methods for understanding how human learning happens. Microgenetic analysis is a labor intensive method that is usually performed in a case study format involving one or a few students. Our goal is to develop a method that will expand possibilities for performing microgenetic analysis over larger data sets, and to expand the scope of research questions that may be asked and answered with such data sets. The below video provides information on the current state of our solution to this educational research problem using computational means.

Playful Talk and Collaborative Learning

The playful talk research project focuses on the creative aspects of robotics learning. We focused on two case studies in this project. In both cases, students learned how to use robotics technologies to solve challenges. Beginning challenges were focused on helping students learn how to use robotic sensors and to do basic programming. Later challenges supported students to solve challenges rooted in a sixth grade science light and heat energy curriculum, in the first case, and to solve FIRST robotics food factor challenges in the second case. The results of our research indicate that youth use playful talk to “try on” potential future identities as well as to regulate small collaborative learning group interactions. Our research extends the Vygotskyan view of the role of play in young children, to the role of playful talk for youth.

Link to Video on Playful Talk

Papers related to our work on microgenetic learning analytics have been presented at AERA 2015 and 2016 as well as CSCL 2015 and 2017 and the International Conference on Computational Thinking Education (CTE 2017).

Sullivan, F.R., & Keith, P.K. (2017). Emergent roles, collaboration and computational thinking in the multi-dimensional problem space of robotics. Proceedings of the 2017 meeting of the International Conference on Computational Thinking Education, Hong Kong, PRC July 13 - 15, 2017. Download here.

Sullivan, F.R., Poza, R., & Keith, P.K. (2017). Girls, robotics learning and internalized stereotypes: is there a relationship? Paper presentation at the 2017 Bi-Annual Meeting of Computer Supported Collaborative Learning, Philadelphia, Pennsylvania June 16 - 20, 2017. Download here.

Sullivan, F.R., Keith, P.K., & Poza, R. (2016). Internalized stereotypes: do they play a role in girls' robotics learning? Paper presentation to the annual meeting of the American Educational Research Association, Washington, DC, April 8-12, 2016. Download here.

Sullivan, F.R., Keith, P.K., & Wilson, N.C. (2016). Learning from the periphery in a collaborative robotics workshop for girls. Paper presentation to the annual meeting of the American Educational Research Association, Washington, DC, April 8-12, 2016. Download here.

Sullivan, F.R., Adrion, W.R. & Keith, P.K. (2015). Microgenetic learning analytics: A computational approach to research on student learning. Paper presentation at the annual meeting of the American Educational Research Association, Chicago, IL, April 16-20, 2015. Download here.

Sullivan, F.R., Keith, P.K., & Wilson, N.C. (2015). Examining power relations in an all girl robotics learning environment. Proceedings of the 2015 Bi-Annual Meeting of Computer Supported Collaborative Learning, Gothenburg, Sweden, June 7 - 11, 2015, vol. 2, 861-863. Download here.