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

基本信息

批准号：
1447788
负责人：
George Karypis
金额：
$ 121.97万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Continuing 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=1447788&HistoricalAwards=false
关键词：
BIGDATA IA DKA Collaborative Research

项目摘要

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.

15年来，六年的高等教育毕业率在59％左右约为59％。不到一半的大学毕业生在4年内结束。这具有高昂的人类，经济和社会成本。国家研究委员会已经确定了开发创新方法以改善学生保留，毕业和劳动力准备的迫切需求。该项目的目的是开发新的计算方法，以分析大量多样的教育和学习数据，以帮助（a）为学生发现成功的学术途径；（b）改善教师的教学法；（c）增强机构的学生持久性和保留率。该项目成果旨在帮助学生选择适合其需求，能力和学习方式的课程，并可能导致（更快）毕业；帮助讲师更好地满足学生需求；并为顾问和机构提供改善保留和持久性所需的分析。拟议的研究将产生新的动态系统建模，协作过滤和多任务学习方法。使用动态状态空间系统对学生知识的演变进行建模是关键的创新。拟议的研究将开发新颖的协作系统识别和为年级预测的Kalman过滤技术。技术创新包括用于不断发展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与潜在变量的受控分组的协作多样性模型。这些创新将融合为三个试点应用：学生的degreeplanner，教师的课程视觉和学术顾问的学生观看。