Learn How to Minimize Misinterpretation of Data Reports and Visualizations
Volumes of data are available to administrators to support decision-making. But that doesn’t mean that what’s been presented is accurate. When data are misused or misconstrued, senior leaders at higher education institutions may make the wrong conclusions, ineffective policies may be enacted, and students may not be successful in completing their academic goals.
Abstract: Data analytics related to student and institutional performance have evolved quite rapidly—and continue to advance—as the field of data science captures more attention across the higher education sector. And while data-informed decisions can help institutional leaders achieve their goals, there are increasing examples of analyses or visualizations that, when presented without the proper framework, result in misinterpretation and inaccurate conclusions. Context is critical, and erroneous deductions may lead to decisions that adversely affect student performance, program development, and policy changes.