Leveraging big data for student data analytics is increasingly integrated throughout university operations from admissions to advising to teaching and learning. Though the possibilities are exciting to consider, they are not without risks to student autonomy, privacy, equity, and educational value. There has been little research showing how…

The problem explored in this study was that the state education agency recommended school board members of low-performing school districts participate in a governance training called student outcomes governance to address student achievement, yet no data suggested the training changed school board members' understanding of how to create policies…

Schooling experiences for Black students in the US have been shaped historically by anti-education laws, mandates, and initiatives that sustain unjust systemic practices and policies. These practices and policies often stagnate academic progress and have led to institutional deficits and the normalization of deficit-orientations towards students…

This case study identifies college outcomes data transparency and accountability beliefs held by policy-makers, higher education associations, advocacy groups, think tanks, and other actors in the federal higher education policy domain, as well as how these beliefs have changed since the last reauthorization of the Higher Education Act in 2008.…

Forty states, including the District of Columbia, have established state longitudinal data systems (SLDS). The data included, intended functions, and methods for establishing SLDS vary from state to state. Further, forty-nine states and the District of Columbia have received one or more federal grant to support SLDS development. The adoption of…

In interactive e-learning environments such as Intelligent Tutoring Systems, there are pedagogical decisions to make at two main levels of granularity: whole problems and single steps. Here, we focus on making the problem-level decisions of worked example (WE) vs. problem solving (PS) and the step-level decisions of elicit vs. tell. More…

Missing values that fail to be appropriately accounted for may lead to reduced statistical power, biased estimators, reduced representativeness of the sample, and incorrect interpretations and conclusions (Gorelick, 2006). The current study provided an ontological perspective of data manipulation by explaining how statistical results can…