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

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一种新的统计模型，该模型将确定需要帮助的学生以及他们需要的帮助类型。该系统建立在个性化医疗中使用的统计模型之上，以确定针对个体患者的最佳医疗干预措施。这项研究将由一个来自统计与数据科学、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，以自动完成学生成功效能研究中的数据收集和处理、数据可视化和总结、数据分析和科学报告等任务。作为这一发展的一部分，个性化治疗效果的概念被引入，作为一种评估干预和/或教学制度有效性的方法，并向学生提供个性化的反馈。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，用于分析大量学生数据。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，研究将开发新的方法，对学生信息系统和学习管理系统收集的数据进行数据清洗，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，能够分析个体和子群体的治疗效果（例如，测试对个体学生和特定子群体学生的教学或其他干预的成功）。统计分析的结果将以仪表板的形式显示，以报告评估改善学生留校率和成绩的干预策略的结果。

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.