Best Practices for Using Analytics to Drive Student Success

Learn about the best practices for making data-driven decisions

In today’s education industry, data analytics plays a crucial role in making sure students prosper.

By implementing analytics, you can gain a deeper understanding of student performance and behavior, which in turn will allow you to make more informed decisions leading to actions that drive student success.

If you are a leader or a CIO within the education industry, understanding the practices and strategies that can be used to make the most of analytics is vital.

How can analytics directly impact students?

Analytics is the process of collecting, analyzing, and interpreting data in order to gain insights and make more informed decisions. In the education industry, analytics can be used to identify areas of improvement and implement strategies to drive student success.

For example, using analytics, you can identify early warning signs of at-risk students and intervene before it’s too late. Analytics can be used to track student progress and provide personalized support, such as targeted tutoring or mentoring, to help students stay on track and succeed.

Analytics also allows schools to address and answer critical questions about a student’s progress, including:

• What happened?

• Why did it happen?

• What will happen?

• How can I make it happen?

With the answers to these questions, you can begin to understand the root cause of problems that are blocking student success and develop targeted solutions to improve student outcomes. Now let’s look at what type of analytics techniques you can use to answer these questions.

4 best practices for using analytics to drive student success

Analytics within the education industry can be broken down into four different approaches: descriptive, diagnostic, predictive, and prescriptive.

Each approach provides unique insights and benefits that, when combined, can be used to improve student outcomes.

1. Descriptive

The most basic form of analytics will help you answer the question, “What happened?” Descriptive analytics is used to understand the past by analyzing historical data to identify patterns and trends.

This approach is particularly useful for identifying areas of improvement and benchmarking progress over time.

Descriptive analytics relies on the use of simple business intelligence (BI) tools and data visualization tools such as charts, graphics, and tables, which are derived from a core data warehouse or queried directly from core systems.

The data used for descriptive analytics in the education sphere includes student information systems (SIS), learning management systems (LMS), human resources, and finance data. This type of data is refreshed on a regular basis and mostly consists of routinely used data elements such as demographics and final grades.

But the data alone is not enough. Human input is required to read the static data provided and apply judgment and experience to make decisions and recommend actions.

Some of the questions you can answer with descriptive analytics include:

• What was the retention rate of first-year students?

• How many students who were accepted actually enrolled?

• What is the completion rate of students within the expected time frame?

• What is the average student loan debt for graduates of a certain program?

2. Diagnostic

Diagnostic analytics is all about answering the question, “Why did it happen?” It is used to understand the underlying causes of past events and make strategic decisions that will affect similar situations in the future based on the patterns and trends identified.

This approach provides a deeper understanding of the root causes of problems and is used to identify opportunities for improvement.

Diagnostic analytics requires the use of advanced data models and business intelligence tools that allow for self-service data exploration and analysis. These tools allow you to drill down into the data and extract insights from within the available models. The data is queried from various sources such as a data warehouse, data mart, or data lake.

With diagnostic analytics, people are required to mine the data, identify patterns, and decide on future actions based on those patterns.

Here are some of the types of questions that can be answered with diagnostic analytics:

• What caused the decrease in second-year enrollment?

• Why are we seeing fewer students accepted into nursing programs?

• Why are students who take remedial courses more likely to drop out?

• Why are students who are the first in their families to attend college more likely to drop out?

3. Predictive

Predictive analytics is about seeing into the future and helps to answer the question, “What will happen?” but since the future is not completely knowable, the question it truly answers is, “What is most likely to happen?”

Predictive analytics can find patterns in data, identify risks and opportunities that could result from future events, and apply targeted actions based on those insights.

To gain predictive insights from data, many statistical techniques can be used. Some of these statistical techniques include logistic and linear regression models, multivariate statistics, predictive modeling, and pattern matching. Both deep and machine learning can be useful in predictive analytics and the data typically comes from an analytical sandbox or data lake.

Human insights are also required in predictive analytics. People are required to determine how the prediction relates to the questions they are trying to answer and create plans to influence the predicted results.

Predictive analytics can help you answer questions like:

• How likely is it that a student will pass a course?

• Is it probable that a student will complete their program of study on time?

• What is the predicted enrollment for a certain program in the next semester?

• How many more students will enroll if we increase the recruiting budget?

4. Prescriptive

The final type of analytics is prescriptive analytics, which will help you answer, “How can I make it happen?” or “What should happen?”

This approach uses artificial intelligence to determine actions that need to be taken in order to achieve a desired outcome. It goes beyond identifying problems and trends to providing solutions and recommendations for future actions.

The techniques used in prescriptive analytics include simulation, complex event processing, neural networks, and machine learning to analyze and model data. These tools allow for sophisticated analysis and modeling of data to identify the best course of action.

The data used for prescriptive analytics can be obtained from various systems, and it is fed into a central storage system like a data hub, a data lake, or a dedicated local data store for the specific application.

Human interaction plays a crucial role in prescriptive analytics and can take two forms. Your analytics solution can provide suggestions for actions, known as decision support, where you receive a recommended course of action but have the freedom to accept or modify it. Or it can fully automate the decision-making process, known as decision automation, and make the decision based on the analysis and carry it out without human intervention.

Some of the questions you can answer with prescriptive analytics include:

• What is the optimal class size for a given course to improve student performance?

• What is the best way to allocate financial aid to improve student success?

• What is the most effective way to increase enrollment in under-represented groups?

• How can we redesign our curriculum to better meet the needs of our students?

Key challenges for using analytics

Using analytics to improve student outcomes requires a strategic approach:

• Identifying the capabilities of your current environment, 

• Aligning these capabilities with the identified needs of the institution

• Defining metrics that address institutional goals related to student success

First, assess the current technological capabilities and resources available within your institution and determine which options will be most effective for achieving your goals. This will allow you to make informed decisions about which data and analytics capabilities to implement and help ensure you are using the most appropriate tools for your needs.

Once the data and analytics options have been identified, it is important to align these capabilities with the needs of your institution. This means determining how your capabilities can address your institution’s specific needs and objectives, as well as how they can be used to inform decisions about which data, tools, and stakeholders’ expectations to prioritize.

Choosing metrics to track can also be a challenge. It is important to define metrics to address institutional objectives around student success and to boost overall student enrollment, retention rates, and on-time completion rates. This includes identifying key performance indicators (KPIs) that are relevant to your institution’s goals such as graduation rates, retention rates, and student satisfaction scores, and tracking these metrics over time to measure progress and identify areas for improvement.

More resources for institutions

With the right data, tools, and human oversight, analytics can be a powerful tool to improve student outcomes and performance, which ultimately leads to a better educational experience for students and a more successful institution.