The project will provide the opportunity for upper elementary students to learn computer science and build strong collaboration practices. Leveraging the promise of virtual learning companions, the project will collect datasets of collaborative learning for computer science in diverse upper elementary school classrooms; design, develop, and iteratively refine its intelligent virtual learning companions; and generate research findings and evidence about how children collaborate in computer science learning and how best to support their collaboration with intelligent virtual learning companions.
There is growing recognition that children can, and should, learn computer science. One of the central tenets of computer science is that it is a collaborative discipline, yet children do not start out with an intrinsic ability to collaborate. The project will provide the opportunity for upper elementary students to learn computer science and build strong collaboration practices. Leveraging the promise of virtual learning companions, the project will address three thrusts. First, the project will collect datasets of collaborative learning for computer science in diverse upper elementary school classrooms. Second, the project will design, develop, and iteratively refine its intelligent virtual learning companions, which support dyads of students in a scaffolded computer science learning environment with an interactive online coding tool. Third, the project will generate research findings and evidence about how children collaborate in computer science learning, and how best to support their collaboration with intelligent virtual learning companions. There will be three families of deliverables: learning activities and professional development, an intelligent learning environment with virtual learning companions, and research evidence that furthers the state of scholarship and practice surrounding the collaborative learning of computer science. The project will situate itself in highly diverse elementary schools in two states, Durham County, North Carolina and Alachua County, Florida. This project is supported by the Discovery Research PreK-12 program, which funds research and development of STEM innovations and approaches.
The project addresses the research question, "How can we support upper elementary-school students in computer science learning and collaboration using intelligent virtual learning companions?" The initial dataset will provide a ground-truth measure of students' collaboration approaches to classroom computer science learning tasks through instrumenting computer labs in elementary schools for collecting dialogue and problem-solving activity. The project will collect triangulating qualitative data to better understand impactful classroom dynamics around dyadic learning of computer science. The technical innovation of the project is the way in which student dyads are supported: each pair of children within the elementary school classroom will interact with a dyad of state of-the-art intelligent virtual learning companions. These companions will enhance the classroom experience by adapting in real time to the students' patterns of collaboration and problem solving to provide tailored support specifically for that pair of students. The virtual learning companions will model crucial dimensions of healthy collaboration through their dialogue with one another, including self-explanation, question generation, attributing challenges to the task and not to deficits in each other, and establishing common ground through uptake of ideas. The project will compare outcomes of computer science learning as measured in two ways: individual pre-test to post-test, and quality of collaboratively produced solutions. The project team will measure collaborative practices through dialogue analysis for the target collaboration strategies, as well as interest and self-efficacy for computer science. The project will utilize a multilevel model design to study the effect of the virtual learning companions on student outcomes. Using speech, dialogue transcripts, code artifact analysis, and multimodal analysis of gesture and facial expression, the team will conduct sequential analyses that identify the virtual learning companion interactions that are particularly beneficial for students, and focus our development efforts on expanding and refining those interactions. They will also identify the affordances that students did not engage with and determine whether to eliminate or re-cast them. The analytics of collaborative process data will once again be augmented with qualitative classroom data from field notes, focus groups, and semi-structured interviews with students and teachers. The themes that emerge will guide subsequent refinement of the environment and learning activities.