Columbia Advisory Group

The role of technology in education has been growing rapidly in recent years, and higher education institutions have been embracing it to improve student success. Information Technology (IT) and data analytics are two tools that higher education officials can utilize to understand the factors that drive student success and allocate resources effectively. In this blog, we will explore how higher education officials can use IT and data analytics to improve student success and the potential benefits these tools can provide educational institutions.

Tracking Enrollment and Retention Rates

One of the most important aspects of higher education is student enrollment and retention rates. Higher education officials can use data analytics to track these rates and gain insights into the effectiveness of their recruitment and retention strategies. By analyzing student data, such as their academic performance and engagement with various programs and services, administrators can develop interventions to support students who are at risk of dropping out.

For example, the University of Maryland University College (UMUC) used predictive analytics to identify students who were at risk of dropping out. The analytics tool used student data such as grades, attendance, and engagement to identify students who were struggling. Based on this information, UMUC developed a student success program that provided customized support to these students. As a result, UMUC saw an 11% increase in retention rates and a 2.3% increase in graduation rates.

Evaluating Student Services

Student services such as tutoring, advising, and counseling are critical for student success. Higher education officials can use data analytics to evaluate these services’ effectiveness and identify improvement areas. By analyzing student usage data and feedback, administrators can allocate resources more effectively and provide better support to students.

For example, the University of Iowa used data analytics to evaluate its tutoring program. By analyzing usage data and feedback from students, the university identified areas for improvement and made changes to the tutoring program. As a result, the university saw a 19% increase in student participation in the tutoring program and a 10% increase in student satisfaction.

Monitoring Financial Performance

Higher education institutions are under constant pressure to manage their finances effectively. Data analytics can help administrators monitor the institution’s financial performance, such as revenue, expenses, and cost per student. This information can help administrators make data-driven decisions about resource allocation and identify areas for cost savings.

For example, the University of Kentucky used data analytics to monitor its financial performance. By analyzing data such as revenue, expenses, and enrollment, the university identified areas for cost savings and developed strategies to reduce expenses. As a result, the university was able to save $48 million over a five-year period.

Predictive Analytics

Predictive analytics can help higher education officials identify students who are at risk of dropping out or falling behind in their studies. By analyzing student data such as grades, attendance, and engagement, administrators can intervene early to support students and improve their chances of success.

For example, Georgia State University used predictive analytics to identify students who were at risk of dropping out. Based on this information, the university developed a student success program that provided customized support to these students. As a result, the university saw a 22% increase in graduation rates and a 6% increase in retention rates.

Personalized Learning

IT applications can be used to provide personalized learning experiences for students. By analyzing student data and preferences, administrators can develop customized learning pathways that meet each student’s unique needs and interests.

For example, Arizona State University used an adaptive learning platform to provide personalized learning experiences to students. The platform provided customized content and assessments to each student by analyzing student data and preferences. As a result, the university saw a 7% increase in student retention rates and a 5% increase in graduation rates.

Research-Based Data

Research-based data supports the potential benefits that IT applications can provide to educational institutions. A study conducted by the EDUCAUSE Center for Analysis and Research found that institutions that effectively use data analytics are more likely to have higher retention rates, graduation rates, and improved student satisfaction. Additionally, a National Center for Education Statistics report found that institutions that use data analytics to support student success are more likely to have higher graduation rates.

It is clear that IT and data analytics can provide significant benefits to higher education institutions. Higher education officials can improve student success and allocate resources more effectively by tracking enrollment and retention rates, evaluating student services, monitoring financial performance, using predictive analytics, and providing personalized learning experiences.

In addition, tracking enrollment, retention, and graduation rates, evaluating student services, and monitoring financial performance through data analytics can be extremely beneficial to educational institutions’ administration. Here are some key benefits of using data analytics for these purposes: