Higher ed institutions are facing increasing pressure to retain their students. With the ever-increasing rise of college tuition, more students and families wonder if it is even worth getting a college degree. Not to mention, colleges face tremendous pressure from state and federal officials who push for students who enter public institutions to earn a degree, especially those from marginalized groups. More than 24 states disburse their state funding on how many students a university graduates, instead of how many it enrolls, so retaining students is becoming more critical to their bottom line. Due to these pressures, some higher ed institutions have turned to predictive analytics. By analyzing demographic and performance data, universities can predict whether students will excel in their courses or require support.
What is predictive analytics?
According to Gartner, “Predictive analytics answer ‘What will happen?’ by recognizing patterns and assessing likely outcomes
using statistical or machine learning techniques. This ability to provide forward-looking insights (not just past and present) provides decision-makers with even more information to support better decisions.”Predictive analytics uses techniques from data mining, statistics, modeling, machine learning, and artificial intelligence (AI), to analyze current data to make predictions about the future.Types of predictive analytic models:Generally speaking, the term predictive analytics is used to mean predictive modeling or scoring data with predictive models and forecasting. Nowadays, people are increasingly using the term to refer to its related analytical disciplines, but it is important to note the models and their definitions. Predictive models: analyze the relationship between the specific performance of a unit in a sample and one or more known attributes or features of the unit. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction in order to guide a decision.: quantifies relationships in data to classify customers or prospects into groups. Descriptive models identify many different relationships between customers or products and can be used to categorize customers by their product preferences and life stage.: describe the relationship between all the elements of a decision—the known data (including results of predictive models), the decision, and the forecast results of the decision—in order to predict the results of decisions involving many variables. Decision models are generally used to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance.
