What is ELDA?
Schools and districts are awash in data, from more traditional data sources such as test scores, grades, attendance, discipline reports and surveys, to more recent innovations such as intelligent tutoring computer systems, computer interaction logfile data, and multimodal instrumented instructional spaces. Education Leadership Data Analytics (ELDA) helps school leaders make sense of the data that are collected on students and schools on a daily basis through applying current big data, data mining, and data science analytic techniques to educational issues that are important to teachers, principals, superintendents, parents, and most importantly, students.
Receiver Operating Characteristic (ROC) Area Under the Curve (AUC): A Diagnostic Measure for Evaluating the Accuracy of Predictors of Education Outcomes
In the education domain, it has become increasingly popular for researchers to use Early Warning Systems (EWS) and Early Warning Indicators (EWI) to predict student outcomes such as high school dropout, college enrollment, STEM degrees, and STEM careers. To see whether the EWS and EWI work well in making these predictions, educational researchers tend to use statistical significance to examine each predictor. However, statistical significance ignores the accuracy, or the duality of sensitivity and specificity, of the predictors. To overcome this problem, Alex J. Bowers and Xiaoliang Zhou (2019) showed the usefulness of ROC AUC in evaluating the accuracy of EWS and EWI indicators of education outcomes.