Learning Research and Development Center
The Learning Research and Development Center (LRDC) advances the science of learning by bringing together researchers from the cognitive, developmental, social, educational, and computational sciences.
I worked in the Future Adaptive Collaborative Educational Technologies Lab (FACETLab) within the LRDC. The goal of the FACETLab is to design empowering educational technologies that facilitate social learning and are adaptive and customizable to the contexts in which they are used. The transdisciplinary work of the lab combines ideas from Artificial Intelligence, Human Computer Interaction, Learning Sciences, Educational Psychology, and Culturally Responsive Teaching.
Projects
Collaborative Learning with Nao Robots in Middle School Mathematics
This project explores social dialogue and entrainment in a teachable robot. In this project, we investigated how to design effective dialogue, gaze, and gesture behaviors in a social robot that supports collaborative learning. The remote study involved individual or dyad student participants, who explained equation-solving problems to a robot. The robot listens to the students and responds by asking questions and stating comments about the problems, all while using its tone of voice to build a connection with the student(s).
While working on Collaborative Learning with Nao Robots in Middle School Mathematics project, I helped with the organization and analysis of video data and transcripts. Before starting the analysis, I organized and cleaned transcripts, and conducted literature reviews about rapport and qualitative coding methods. Once everything was organized, I developed a coding scheme and codebook to qualitatively code transcripts. I assisted in quantitative coding and statistical analysis of data. For this project, our work was published as "Exploring Social Contagion in a CSCL Environment with a Social Robot" in the European Association of Research on Learning and Instruction (EARLI) 2023.
Gesture Elicitation of Mathematics
Within the Collaborative Learning with Nao Robots in Middle School Mathematics project, I also worked on the Gesture Elicitation study, which asynchronously researched how a robot’s non-verbal behaviors, along with its dialogue and gaze might prompt students’ thinking and expand their understanding of mathematical concepts. I assisted with searching for meta-analyses on gesture priming, examining how non-verbal cues could help students understand math. I searched through publications of previous gesture and mathematics-related studies to understand the predicted effect size. I also collaboratively designed the Gesture Elicitation study design using Miro to help visualize the study steps, recorded study components, and migrated content between video platforms. For this study, I presented a poster as part of PittInclusion’s 2021 cohort. PittInclusion is an undergraduate research program in collaboration with the Google ExploreCSR (Computer Science Research) program.
For this study, Mathematical-related gestures were collected through a video survey, in which participants were given randomized statements containing mathematical words. Participants were asked to depict keywords within these statements using strictly gestures. We were able to implement the collected gestures in the robot, so it can use human-like gestures to teach mathematics efficiently. Using gestures will keep students more engaged in an educational setting and help them better understand mathematical concepts.
UbiCoS
The UbiCoS (Ubiquitous Collaboration Support) project aims to explore how technology can adaptively support the process of giving help to one’s peers as students move between learning activities, by leveraging a student’s help-giving history and current behaviors to provide personalized prompts that target learning and motivational outcomes. Designed to support middle school mathematics students in a classroom learning session with the help of a digital textbooks platform, the study will be considered as part of the class curriculum.
I observed study sessions making sure they were being recorded, conducted attendance, and took detailed notes of each class session, outlining the approximate time each activity took. I also worked on the analysis and coding of study data. For this project, our work “Exploring the Use of Badges as Cross-Platform Collaborative Support” was published in the 2023 IEEE International Conference on Advanced Learning Technologies (ICALT).
