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

EAGER：基于学生序列模型的交互式学习分析框架，用于了解学生、保留率和毕业时间

• 批准号: 1820862
• 负责人: Mary Lou Maher
• 金额: $ 29.85万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820862&HistoricalAwards=false
• 关键词: EAGER; Interactive; Learning; Analytics; Framework

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.

这是一个个性化的学术咨询项目，目的是更好地识别学生在留校和毕业过程中的风险和成功。个性化是通过使用一种新的数据建模方法开发的交互系统来实现的。辅导员将获得学生所选课程、实验和部分的顺序，以及成绩和任何其他可用的评估数据。然后，这些信息被提供给数据挖掘应用程序，该应用程序识别学生的成功程度，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来自教师、导师和学术领导层的交互输入来准确地模拟学生的成绩。更准确地说，交互框架通过将领域专家包括在从异类和纵向学生数据中的数据驱动的发现和决策中，使得能够发现可操作的知识以提高学生的成功。该方法是在单一的交互式用户体验中集成和迭代特征提取、分析和解释过程。通过使用数据和数据模式的探索性交互式可视化，目标用户社区，包括学术领导层、教师和顾问，将被授权探索更广泛的有意义的假设，并根据正在收集的关于学生表现和校园生活的大量而复杂的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，这些政策、课程改革和干预措施可以更主动地解决大学中的特定关键问题，而不是传统的分析可以提供的影响留校、毕业时间和学生成功的能力。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Framework for Interactive Exploratory Learning Analytics

• DOI: 10.1007/978-3-319-91152-6_25
• 发表时间: 2018-07
• 期刊: 2008 International Conference on Computer Science and Software Engineering
• 作者: M. Mahzoon;M. Maher;Omar Eltayeby;Wenwen Dou;Kazjon Grace

Making Sense of Student Success and Risk through Unsupervised Learning and Interactive Storytelling

通过无监督学习和互动讲故事了解学生的成功和风险

• 发表时间: 2020
• 期刊: Proceedings
• 作者: Ahmad Al-Doulat, Nur