CAREER: CS-CLIMATE: Fostering Collaborative Dialogue for Rigorous Learning and Diverse Student Retention in Computer Science

大量的证据表明，协作学习对计算机科学专业的学生有很多好处，但人们越来越认识到，无论是协作学习本身，还是它所处的创新课程，都不是解决计算管道问题的“灵丹妙药”。与一刀切的解决方案相比，协作学习高度依赖于协作者的特征和细粒度的交互。这个项目的首要研究问题是：我们能识别和支持的方面，特别是有效的促进学习，认同感，动机，并继续参与不同的计算机科学学习者的协作对话？该项目通过三项活动调查这一问题。首先，将收集一组丰富的计算机科学协作学习数据，利用ASCEND学习环境，该环境支持远程协作，包括文本自然语言对话、同步代码编辑以及两个或多个协作者的集成存储库控制。数据将在三个合作机构收集：北卡罗来纳州州立大学，梅雷迪思学院（全女子机构）和佛罗里达A M大学（少数民族服务的大学，90%的非洲裔美国人入学），并将包括学生的性别，种族/民族，个性特征和成就目标取向，以及学习，计算身份感，动机和参与等成果的措施。 其次，该项目将研究协作对话的细粒度方面，这些方面对不同的计算机科学学习者特别有效，以创建细粒度，理论上知情的模型，这些模型捕获与学习，身份发展，动机和参与相关的协作对话和问题解决现象。 第三，该项目将实施和评价循证教学支助，以促进有效的合作对话。预计由此产生的教学支持将大大改善学习，认同感，动机，并继续参与学生整体，特别是妇女和非洲裔美国学生。此外，该项目将产生细粒度的顺序分析和丰富的定性结果，进一步了解不同的学生如何学习计算。

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.