BIGDATA: IA: DKA: Collaborative Research: Learning Data Analytics: Providing Actionable Insights to Increase College Student Success

大数据：IA：DKA：协作研究：学习数据分析：提供可行的见解以提高大学生的成功

• 批准号: 1447489
• 负责人: Huzefa Rangwala
• 金额: $ 76.62万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1447489&HistoricalAwards=false
• 关键词: BIGDATA; IA; DKA; Collaborative; Research

The six-year higher-education graduation rate has been around 59% for over 15 years; less than half of college graduates finish within 4 years. This has high human, economic and societal costs. The National Research Council has identified a critical need to develop innovative approaches to improve student retention, graduation, and workforce-preparedness. The objective of this project is to develop new computational methods to analyze large and diverse types of education and learning data to help (a) discover successful academic pathways for students; (b) improve pedagogy for instructors; and (c) enhance student persistence and retention for institutions. The project outcomes are designed to help students select courses that fit their needs, capabilities, and learning styles, and are likely to lead to (faster) graduation; help instructors to better meet student needs; and give advisors and institutions the analytics needed to improve retention and persistence. The proposed research will produce new dynamical system modeling, collaborative filtering, and multi-task learning methods. Modeling the evolution of a student's knowledge using a dynamical state-space system is a key innovation; the proposed research will develop novel collaborative system identification and collaborative Kalman filtering techniques for grade prediction. Technical innovations include supervised learning approaches for evolving datasets, such as linear and non-linear multi-task learning and collaborative multi-regression models with controlled grouping of the latent variables. These innovations will coalesce into three pilot applications: DegreePlanner for students, CourseInsights for instructors, and StudentWatch for academic advisors.

六年制高等教育的毕业率15年来一直保持在59%左右;不到一半的大学毕业生在4年内完成学业。这造成了高昂的人力、经济和社会代价。国家研究理事会已经确定了一个迫切需要开发创新的方法，以提高学生的保留，毕业和劳动力准备。该项目的目标是开发新的计算方法，分析大量不同类型的教育和学习数据，以帮助（a）为学生发现成功的学术途径;（B）改进教师的教学方法;（c）提高学生的持久性和机构的保留率。项目成果旨在帮助学生选择适合他们的需求，能力和学习风格的课程，并可能导致（更快）毕业;帮助教师更好地满足学生需求;并为顾问和机构提供提高保留率和持久性所需的分析。该研究将产生新的动态系统建模、协同过滤和多任务学习方法。使用动态状态空间系统的学生的知识的演变建模是一个关键的创新，拟议的研究将开发新的协作系统识别和协作卡尔曼滤波技术的等级预测。技术创新包括用于不断变化的数据集的监督学习方法，例如线性和非线性多任务学习以及具有潜在变量受控分组的协作多元回归模型。这些创新将合并为三个试点应用程序：面向学生的DegreePlanner，面向教师的CourseInsights和面向学术顾问的StudentWatch。