A Machine Learning Student Behavior Model to Identify Struggling Students in Introductory Computer Science Courses

机器学习学生行为模型，用于识别在计算机科学入门课程中遇到困难的学生

• 批准号: 2125959
• 负责人: Monica Vroman
• 金额: $ 33.96万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125959&HistoricalAwards=false
• 关键词: Machine; Learning; Student; Behavior; Model

This project, administered by Rutgers University New Brunswick, builds capacity in STEM Education Research (SER) through the investigator’s participation in intensive professional growth experiences and application of their new knowledge in the design and implementation of a SER study. The investigator will build proficiency in mixed methods research by engaging in focused coursework, professional conferences, and mentoring by SER experts over the life of the project. In tandem with professional development efforts, the investigator will design and test a machine learning tool tailored to assess student progress in real time in computer science coursework. Once the project goals are realized, the developed tool holds potential for improving student outcomes in computer science, an area of critical need in the United States workforce. The tool will be adaptable for use across disciplines to support student STEM outcomes overall, while the researcher will be prepared to further advance the knowledge base in effective STEM education practices.The project investigator’s professional growth activities, customized via a gap assessment, will focus on qualitative research methods, learning theory, and learning analytics. Through scholarly study that includes regular formative meetings with experts in SER and engagement in focused coursework and professional experiences, the investigator will build expertise to enable the development of a machine learning tool employing behavioral models that predict student success in computer science courses. Detection of student struggles enroute will promote the timely use of intervention strategies to enhance success across demographic groups, thereby improving representation in vital STEM disciplines such as the computer sciences. This project is supported by the ECR Building Capacity in STEM Education Research competition of the EHR Core Research (ECR) program. ECR funds fundamental STEM education research projects that focus on STEM learning and learning environments, broadening participation in STEM fields, and STEM professional workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目由罗格斯大学新不伦瑞克（New Brunswick）管理，通过研究者参与密集的专业成长经验以及将新知识应用于SER研究的设计和实施，从而建立了STEM教育研究（SER）的能力。研究人员将通过在项目一生中参与SER专家的重点课程，专业会议和心理工作，从而建立混合方法研究的水平。在与专业开发工作的同时，研究人员将设计和测试用于在计算机科学课程中实时评估学生进度的机器学习工具。一旦实现了项目目标，开发的工具就有改善计算机科学的学生成果的潜力，这是美国劳动力中至关重要的领域。该工具将适用于跨学科的使用，以支持学生STEM的总体成果，而研究人员将准备进一步促进有效的STEM教育实践中的知识基础。项目研究者的专业成长活动，通过差距评估进行定制，将重点关注定性研究方法，学习理论和学习分析。通过科学研究，包括与专家在集中课程和专业经验方面的专家进行定期成立会议，研究人员将建立专业知识，以实现开发机器学习工具的开发，该工具采用行为模型来预测学生在计算机科学课程中的成功。检测学生斗争将促进及时使用干预策略来增强人口统计组的成功，从而改善重要的茎科学学科（例如计算机科学）的代表性。 EHR核心研究（ECR）计划的STEM教育研究竞赛中的ECR建筑能力支持该项目。 ECR资金基本的基本STEM教育研究项目侧重于STEM学习和学习环境，扩大对STEM领域的参与以及STEM Professional劳动力发展。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估标准来通过评估来获得的支持。