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

大量证据表明，协作学习对计算机科学专业的学生有很多好处，但越来越多的人认识到，协作学习本身以及协作学习可能所在的创新课程，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不同，协作学习高度依赖于合作者的特征和细粒度的交互。这个项目的主要研究问题是：我们能否识别和支持协作对话的各个方面，这些方面对于促进不同计算机科学学习者的学习、认同感、动机和持续参与特别有效？该项目通过三个活动来研究这个问题。首先，将利用Ascend学习环境收集丰富的计算机科学协作学习数据，该环境支持通过文本自然语言对话、同步代码编辑和两个或更多合作者的集成存储库控制进行远程协作。数据将在三所合作机构收集：北卡罗来纳州立大学、梅雷迪斯学院(一家全女性机构)和佛罗里达农工大学(一所为少数族裔服务的大学，90%的非裔美国人入学)，数据将包括学生的性别、种族/民族、个性特征和成就目标导向，以及学习、计算认同感、动机和参与度等结果衡量标准。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，以便创建细粒度的、理论上知情的模型，捕捉与学习、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以证据为基础的教学支助，以促进有效的协作对话。预计由此产生的教学支持将显著改善学生的学习、认同感、动机和持续参与，特别是对女性和非裔美国学生。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.