BIGDATA: IA: Acting on Actionable Intelligence: A Learning Analytics Methodology for Student Success Efficacy Studies

大数据：IA：根据可行的情报采取行动：学生成功效能研究的学习分析方法

• 批准号: 1633130
• 负责人: Richard Levine
• 金额: $ 109.62万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633130&HistoricalAwards=false
• 关键词: BIGDATA; IA; Acting; Actionable; Intelligence

The research supported by this project will study how instructors, administrators, and education researchers take advantage of rich student and student performance data collected by the university. The data will be used in the development of a new statistical model that will identify students in need of help and the sort of help that they need. The system is built upon statistical models that are used in personalized medicine to determine the best medical interventions for an individual patient. The research will be carried out by an interdisciplinary team from statistics and data science, institutional research, instructional technology, and information technology and they will develop a learning analytics methodology to automate the tasks of data collection and processing, data visualizations and summaries, data analysis, and scientific reporting in student success efficacy studies. As part of this development, the concept of individualized treatment effects is introduced as a method to assess the effectiveness of interventions and/or instructional regimes and provide personalized feedback to students.More specifically the research goal of the project is to develop and test new statistical methods for analyzing large sets of student data. The data sets to be analyzed and tested arise from administrative student data collected by San Diego State University. Additionally, the research will develop new methods of data cleaning for the student information system and learning management system data collected by the university to make the entire analysis procedures more efficient. The technical contribution is to utilize a new random forest of interaction trees machine learning method that enables the analysis of treatment effects for individuals and for subgroups (e.g., testing the success of a pedagogical or other intervention for both individual students and for specific subgroups of students). The results of the statistical analysis will be displayed as dashboards to report the findings for the assessment of intervention strategies in improving student retention and performance.

该项目支持的研究将研究教师、管理人员和教育研究人员如何利用大学收集的丰富的学生和学生表现数据。这些数据将用于开发新的统计模型，该模型将识别需要帮助的学生以及他们需要的帮助类型。该系统建立在用于个性化医疗的统计模型之上，以确定针对个体患者的最佳医疗干预措施。该研究将由来自统计和数据科学、机构研究、教学技术和信息技术的跨学科团队进行，他们将开发一种学习分析方法，以自动执行数据收集和处理、数据可视化和摘要、数据分析等任务，以及学生成功效能研究的科学报告。作为这一发展的一部分，引入了个体化治疗效果的概念，作为评估干预措施和/或教学制度的有效性并向学生提供个性化反馈的方法。更具体地说，该项目的研究目标是开发和测试新的统计方法分析大量学生数据的方法。要分析和测试的数据集来自圣地亚哥州立大学收集的行政学生数据。此外，该研究还将针对大学收集的学生信息系统和学习管理系统数据开发新的数据清理方法，使整个分析过程更加高效。技术贡献是利用一种新的交互树随机森林机器学习方法，该方法能够分析个人和亚组的治疗效果（例如，测试针对个别学生和特定学生亚组的教学或其他干预措施的成功与否） ）。统计分析的结果将显示为仪表板，以报告评估提高学生保留率和表现的干预策略的结果。

项目成果

Constructing Multivariate Survival Trees: The MST Package for R

• DOI: 10.18637/jss.v083.i12
• 发表时间: 2018-02-01
• 期刊: JOURNAL OF STATISTICAL SOFTWARE
• 影响因子: 5.8
• 作者: Calhoun, Peter;Su, Xiaogang;Fan, Juanjuan
• 通讯作者: Fan, Juanjuan

Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies

• DOI: 10.1002/sta4.457
• 发表时间: 2022-12-01
• 期刊: STAT
• 影响因子: 1.7
• 作者: Li, Luo;Levine, Richard A.;Fan, Juanjuan
• 通讯作者: Fan, Juanjuan

A learning analytics case study: On class sizes in undergraduate writing courses

学习分析案例研究：本科写作课程的班级规模

• DOI: 10.1002/sta4.527
• 发表时间: 2023
• 期刊: Stat
• 影响因子: 1.7
• 作者: Levine, Richard A.;Rivera, Patricia E.;He, Lingjun;Fan, Juanjuan;Bresciani Ludvick, Marilee J.
• 通讯作者: Bresciani Ludvick, Marilee J.

Estimating a Dose-Response Relationship in Quasi-Experimental Student Success Studies

估计准实验学生成功研究中的剂量反应关系

• DOI: 10.1007/s40593-021-00280-0
• 发表时间: 2022
• 期刊: International Journal of Artificial Intelligence in Education
• 影响因子: 4.9
• 作者: Shao, Lucy;Levine, Richard A.;Guarcello, Maureen A.;Wilke, Morten C.;Stronach, Jeanne;Frazee, James P.;Fan, Juanjuan
• 通讯作者: Fan, Juanjuan

Balancing Student Success: Assessing Supplemental Instruction Through Coarsened Exact Matching

平衡学生的成功：通过粗略的精确匹配评估补充教学

• DOI: 10.1007/s10758-017-9317-0
• 发表时间: 2017
• 期刊: Knowledge and Learning
• 作者: Guarcello, Maureen A.;Levine, Richard A.;Beemer, Joshua;Frazee, James P.;Laumakis, Mark A.;Schellenberg, Stephen A.
• 通讯作者: Schellenberg, Stephen A.