Data-driven decision-making in education is not just for people with advanced math degrees. That’s one key takeaway from Amelia Parnell’s book You Are a Data Person: Strategies for Using Analytics on Campus.
“In the most simple way, data is information. That data can be robust, it can be comprehensive, or it could be very simple,” she explained in an interview with Inside Higher Ed.
Over the past decade, data has increasingly played a central role in decisions related to day-to-day student performance, financial aid distribution, recruitment, and curriculum. At Amarillo College, for example, student surveys inform the distribution of emergency aid. Georgia State University experienced a boost in graduation rates after it started using predictive analytics for academic advising.
If your institution is just getting in on the data and analytics game, this guide is for you. You’ll learn how colleges and universities are benefiting from new approaches today so you can get the inspiration you need.
But before delving into a few examples and best practices, let’s define data-driven decision-making—a term that has been used in different ways.
Harvard Business School defines data-driven decision-making as: “the process of using data to inform your decision-making process and validate a course of action before committing to it.” Simply put, instead of relying on gut feeling or intuition to make decisions, people use data.
However, some academics and analytics practitioners prefer to use the term “data-informed decision-making” to emphasize that people don’t always accept a recommendation produced by a mathematical formula or machine learning model. For example, considering how student loan forgiveness will impact institutional strategies requires some political acumen and timing considerations.
For this guide, however, we’ll use data-driven decision-making as an umbrella term for any type of decision where data plays a central role, whether the decision maker draws on other resources (like professional experience). This also covers decisions that are automated and made by a computer program.
From helping institutions intervene with at-risk students to improving recruitment and marketing strategies, there is no limit on data-driven decision-making’s potential impact in the higher education sector.
There are considerations for using this data responsibly, however. Before rolling out an analytics solution to support data-driven decision-making, forward-thinking higher learning leaders are creating governance policies, addressing data integrity, and engaging faculty in data literacy training.
Privacy is another consideration. At Pima Community College, faculty members can see student demographics, subject codes, course numbers, and instructional delivery methods, but more sensitive data is kept in restricted folders. With a strong culture in place that respects student and faculty privacy, higher learning institutions can deliver data-driven decision-making responsibly and effectively.
Here are three examples of what data-driven decision-making in education looks like in practice and how it benefits institutions:
Faculty are on the front lines of student success, but until the first assignment or quiz is submitted, they are often flying blind in terms of who is succeeding and who is falling behind. Data-generated insights presented via student analytics identify at-risk students before key assessments and provide intervention earlier with actionable insights.
For example, South Piedmont Community College leverages the analytics that come with the BibliU e-textbook platform to gain insights on total material costs, overall student success, and course material engagement.
Before using the solution, professors at the college had limited insights into which texts were aiding students in their learning and which ones weren’t as integral in achieving learning outcomes. Now, rather than waiting for students to speak up, they can take the initiative to spot problems and provide help.
When higher learning leaders make curriculum decisions based on instinct, it can lead to a disconnect between what the institution is offering and what students need to be successful in their careers.
Many course offerings don’t align with what students want, and according to the Strada Education Network, confidence in the value of education has been sliding over the past two years. But data-driven decision-making can help institutions adjust their offerings to meet student demand.
Cleveland Community College, for example, uses digital dashboards to inform its curriculum. If leaders see that a class is filling up, they can see if an instructor is available and open up new sections for the course. The approach has paid off: Amidst plummeting enrollments nationwide, the college saw a boost in enrollment between 2017 and 2021.
When this kind of approach is combined with labor market analytics from providers such as Lightcast, leaders can build attractive programs that set students up for success.
Many institutions are evolving to give students the kind of experiences they expect from living in an Amazon world, where everything is personalized and quick. According to Educause’s Top 10 IT Issues report for 2022 report: “The higher education we deserve is one in which institutions embrace students as their primary customers.”
With data, institutions can create personalized experiences to meet the individual needs of current and prospective students. For example, a mature student with a full-time job who is browsing a college website and is interested in enrolling could be presented with information related to scheduling flexibility.
Data can also enable institutions to customize instructional approaches for individual learners. Many universities are leveraging adaptive learning platforms to craft personalized paths for students based on performance.
This is an example of how the education system can leverage data to meet the unique needs of individual students rather than forcing all students into the same mold.
Data-driven decision-making in education is a relatively new phenomenon. But this is not a fad—it’s here to stay.
The time to get the ball rolling with data and analytics in higher education is now. The pressure is on for higher learning leaders to move the needle on several outcomes at once: Students and parents want higher employability, governments want institutions to operate efficiently, and advocacy groups are calling for more equity.
Those higher learning leaders who don’t get on the bandwagon now risk falling behind in terms of enrollment and student success.
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