EAGER: An Interactive Learning Analytics Framework based on a Student Sequence Model for understanding students, retention, and time to graduation

这是一个个性化学术建议的项目，目的是更好地识别学生在高等教育中保留和毕业的风险和成功。个性化是通过使用一种新的数据建模方法开发交互式系统来完成的。指导老师会得到学生修过的课程、实验和部分的顺序，以及成绩和其他可用的评估数据。然后将此信息提供给数据挖掘应用程序，该应用程序确定学生的成功程度，并预测学生为了取得学业成功可能需要的任何额外帮助。数据挖掘应用程序使用机器学习技术和教师、顾问和学术领导的交互式输入来准确地模拟学生的成绩。更准确地说，交互式框架允许发现可操作的知识，通过将领域专家纳入数据驱动的发现和决策中，从异构和纵向的学生数据中提高学生的成功。该方法是在单个交互式用户体验中集成和迭代特征提取、分析和解释过程。通过使用探索性数据和数据模式的交互式可视化，包括学术领导、教师和顾问在内的目标用户社区将被授权探索更广泛的有意义的假设，并根据收集到的关于学生表现和校园生活的大量复杂数据，得出具体的可操作的见解。这将改变制定政策、课程改革和干预措施的能力，这些措施可以比传统分析更主动地解决大学中特定的关键问题，从而影响留校率、毕业时间和学生的成功。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This is a project to personalize academic advising with the goal of better identification of student risk and success in retention and graduation in higher education. The personalization is accomplished through the development of an interactive system using a novel approach to data modeling. The advisor is given the sequence of courses, labs, and sections that the student has taken, along with grades and any other assessment data that is available. This information is then fed to a data mining application that identifies the degree of success that the student has had and a prediction of any additional assistance the student may require in order to achieve academic success. The data mining application uses machine learning technology and interactive input from faculty, advisors, and academic leadership to accurately model student achievements.More precisely, the interactive framework enables the discovery of actionable knowledge to improve student success by including the domain experts in data-driven discovery and decision-making from heterogeneous and longitudinal student data. The approach is to integrate and iterate the feature extraction, analytics, and interpretation processes within a single interactive user experience. Through the use of explorative interactive visualization of data and data patterns, the target user communities, including academic leadership, faculty, and advisors will be empowered to explore a broader range of meaningful hypotheses and derive specific actionable insights given the large and complex data that is being collected about students' performance and campus life. This will transform the ability to create policy, curriculum changes, and interventions that can address specific critical issues in universities more proactively than traditional analyses can provide for affecting retention, time to graduation, and student success.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.