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

协作研究：利用机器学习改变软件工程教育中团队合作有效性的理解、评估和预测

• 批准号: 1140191
• 负责人: Shihong Huang
• 金额: $ 3.37万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1140191&HistoricalAwards=false
• 关键词: Collaborative; Research; Transforming; Understanding; Assessment

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.

为了满足软件工程行业的需求，该项目正在开发强大的基于机器学习的方法，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合作。该项目包括以下活动。 客观和定量的团队合作数据正在收集的学生活动，在三个地理上遥远的机构（旧金山弗朗西斯科州，佛罗里达大西洋和富尔达大学）正在进行的，联合授课的本科计算机科学类。新的机器学习工具正被应用于这些数据，以发现模型、规则和指标，这些模型、规则和指标定义了学生在获得团队合作技能方面的成功，并通过识别团队合作成果的预测因素，促进对处于风险中的团队的早期干预。在外部评估人员的投入下，正在完善该项目中开发的方法和工具，并将其分发给教育工作者，以供早日采用。 该项目推进了软件工程领域的新工具，用于评估学生的软件工程团队合作技能。该项目是第一个应用新型机器学习技术来评估和预测学生学习这些技能的项目。在全球合作的时代，该项目还评估了在全球分布的团队中发展团队合作技能的影响。参与的美国大学为高度多样化的学生群体提供服务;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，该项目有助于多样化和保持美国软件工程劳动力的竞争力。来自整个加州州立大学系统和旧金山弗朗西斯科社区学院的软件工程教育工作者的培训研讨会是该项目的一部分。