A rich body of evidence suggests that collaborative learning holds many benefits for computer science students, yet there is growing recognition that neither collaborative learning itself, nor the innovative curricula in which it may be situated, are "magic bullets" for solving computing's pipeline problem. In contrast to being a one-size-fits-all solution, collaborative learning is highly dependent upon characteristics of the collaborators and on fine-grained interactions. The overarching research question of this project is: Can we identify and support the facets of collaborative dialogue that are particularly effective for fostering learning, sense of identity, motivation, and continued engagement for diverse computer science learners?
The project investigates this question in three activities. Firstly, a rich set of computer science collaborative learning data will be collected, leveraging the ASCEND learning environment, which supports remote collaboration with textual natural language dialogue, synchronized code editing, and integrated repository control for two or more collaborators. Data will be collected at three partnering institutions: North Carolina State University, Meredith College (an all-women's institution), and Florida A&M University (a minority-serving university with 90% African American enrollment), and will include student characteristics of gender, race/ethnicity, personality profile, and achievement goal orientation, as well as measures of outcomes such as learning, sense of computing identity, motivation, and engagement. Secondly, the project will examine the fine-grained facets of collaborative dialogue that are particularly effective for diverse computer science learners, in order to create fine-grained, theoretically informed models that capture collaborative dialogue and problem solving phenomena associated with learning, identity development, motivation, and engagement. Thirdly, the project will implement and evaluate evidence-based pedagogical support for fostering effective collaborative dialogue.
It is expected that the resulting pedagogical support will significantly improve learning, sense of identity, motivation, and continued engagement for students overall, and for women and African American students in particular. In addition, the project will produce fine-grained sequential analyses and rich qualitative findings that further the state of knowledge about how diverse students learn computing.