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

San Diego State University Foundation, San Diego CA

Investigators

Abstract

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.