Elasticity Theory of Learning Growth in the 21st Century

My study provides a framework that is based on the ecological framework to understand the dynamic interrelations among various personal and environmental factors. I developed a theory in this dissertation that has the potential to include all the variables (+6000 variables) that the National Education Longitudinal Study of 2002, NELS: 2002, contains. The NELS:2002 data includes surveys from students, teachers, parents, principals, and administrators in a sequence of data collection. The Meinshausen-Bühlmann, MB, algorithm (high-dimensional graph model) selects the variables that can predict a target variable of choice through a lasso regression process. The MB algorithm produces a graph that demonstrates the conditional dependence and independence across all the variables under study. In order to connect funding to learning, the elasticity theory analysis will provide guidelines in the process of selecting the elements that have the highest return on investment. The framework in this dissertation provides a broad scale of data analysis and different approaches to interpret statistics based on the variable’s elasticity. The theory in this dissertation provides a new approach to the analysis of complex data such as the NELS:2002 (+6000 variables, and 16179 entries). This new approach has the potential to change the traditional data analytics landscape across all industries especially in education by including the elasticity theory as an additional factor to interpret the statistical results.