Collaborative Research: Transforming the Understanding, Assessment and Prediction of Teamwork Effectiveness in Software Engineering Education using Machine Learning

为了响应软件工程行业的需求，该项目正在开发基于机器学习的强大方法，以了解、评估和预测学生在全球分布的团队中学习软件工程团队工作的情况。该项目包括以下活动。在三所地理位置遥远的机构(旧金山州立大学、佛罗里达大西洋大学和富尔达大学)正在进行的联合授课的本科计算机科学课程中，正在收集关于学生活动的客观和定量的团队合作数据。新的机器学习工具正被应用于这些数据，以发现定义学生在获得团队合作技能方面的成功的模型、规则和衡量标准，并通过识别团队合作结果的预测因素，促进对处于风险中的团队的早期干预。在外部评估人员的参与下，正在改进该项目中开发的方法和工具，并将其传播给教育工作者，以便早日采用。该项目通过评估学生软件工程团队合作能力的新工具，推动了软件工程领域的发展。这个项目是第一个应用新的机器学习技术来评估和预测学生学习这些技能的项目。在全球协作的时代，该项目还在评估在全球分布的团队中发展团队合作技能的影响。参与的美国大学服务于高度多样化的学生群体；因此，该项目正在准备大量在STEM领域代表性不足的学生进入软件工程专业，并成功应对在全球分布的团队中有效沟通的挑战。因此，该项目有助于美国软件工程劳动力的多样化和保持竞争力。来自加州州立大学系统和旧金山社区大学的软件工程教育者培训研讨会是该项目的一部分。

In response to demands of the software engineering industry, this project is developing powerful machine learning-based methods to understand, assess and predict student learning of software engineering teamwork across globally-distributed teams. The project includes the following activities. Objective and quantitative teamwork data are being collected on student activities in ongoing, jointly-taught undergraduate computer science classes at three geographically-distant institutions (San Francisco State, Florida Atlantic, and Fulda Universities). Novel machine learning tools are being applied to these data to discover models, rules and metrics that define student success in acquiring teamwork skills and that facilitate early intervention for teams at risk by identifying predictors of teamwork outcomes. With input from external evaluators, the methods and tools developed in this project are being refined and disseminated to educators for early adoption. The project advances the field of software engineering with new tools for assessing student software engineering teamwork skills. This project is the first to apply novel machine learning techniques to assess and predict student learning of these skills. In the era of global collaboration, the project is also assessing the impact of developing teamwork skills within globally-distributed teams. The participating US universities serve highly-diverse student populations; thus the project is preparing large numbers of students underrepresented in STEM fields to enter the software engineering profession and to successfully meet the challenges of communicating effectively in globally-distributed teams. As a result, the project is contributing both to diversifying and to maintaining the competitiveness of the US software engineering workforce.A training workshop for software engineering educators from across the California State University system and from a San Francisco community college is part of the project.