Student enrollment

According to recent data, the enrollment rate for Spring dropped by 4.7% than what was seen last year. This is just for undergraduate enrollments. Overall, the admission status continues to decrease. Universities are coming up with various techniques to curb these decreasing rates, which is primarily an after-effect of the pandemic. Analytics is emerging as a significant strategic solution to curb enrollment drops. When combined with AI, advanced analytics can be a game-changer for universities, helping them avoid the looming enrolment crisis.

The impact of the pandemic on universities

With the enrollment rates rising approximately 4.4% last year, universities expected much better growth in the rates this year. However, the enrollment rates for undergraduates dropped by 4.7% in the Spring of 2022 admissions. In addition, the graduate and professional enrollment rates have dropped by 1% compared to 2021. The decline in enrollment rate is surprising because graduate and professional student enrollment held a bright spot even during the pandemic. The drop in the rates has escalated concerns about whether there is a fundamental shift in how students approach the value of universities.

Is the pandemic solely responsible for the enrollment drops?

The pandemic undoubtedly contributed to the recent fall in enrollment rates, but it was not the sole factor behind the decline. Defining variables such as location and demographics have been long identified as the most common reasons for enrollment drops. In addition, other factors like staffing issues, increase in tuition fees, and so on also contribute to the decline in enrollment rates. However, a major, underrated reason is the lack of using tech-driven strategies, leading to inefficient management.

Where does the gap occur?

Universities or academic institutions generally store their data in heterogeneous sources, such as Excel spreadsheets, PDFs, CRM, and so on. The data is then divided into multiple pie charts, scattergrams, graphs, etc., which makes the process of fetching data cumbersome. Additionally, bringing insights or tracking things becomes even more complicated when the data increases.

Besides the data storage issue, most university administrators need to be more tech-savvy, which can be a little challenging for them. This is one of the most common setbacks faced from an enrollment and recruitment point of view. All these challenges combined lead to hurdles, such as enhancing the student experience, difficulty in retaining students, helping graduates with their careers, and so on.

The transformative potential of data analytics in higher education

According to a survey on Global Big Data Analytics in the education sector, it is reported that the market size for analytics is expected to reach approximately $48 billion by 2027. The market growth will exponentially rise by 21% CAGR in the next 5 years.

Data analytics has helped institutions track and analyze students and streamline educational activities that produce a large amount of data. As per EDUCAUSE, nearly 69% of institutions view analytics as a priority for major departments or programs, 28% reported analytics as a priority for the whole organization, and 6% reported that analytics is not a priority. However, the rapid growth of analytics in higher ed seems to close this 6% in the future.

A strategic comparison of data analytics market growth for 10 years (2017 - 2027).

Major ways how data analytics can curb enrollment drops

1) Understanding student demographics

One of the major ways in which data analytics help curb enrollment drops is by analyzing student demographics. Understanding this aspect of student retention is crucial. Analytics can help institutions analyze demographics like age, ethnicity, and gender. Based on this, enrollment officers can filter out students; if they prefer online classes, on-campus classes, or a mix of the two. As per the EDUCAUSE survey, universities can benefit from approximately 86% of analytics potential in understanding demographics.

2) Identifying students at risk of dropping out

Data analytics can help universities by identifying students who are at risk of dropping out. It analyzes the performance of the student over the last two-three months, which can help improve the class schedule for them. Thereby assisting students in focusing on their weaker subjects instead of dropping out. That said, conducting such analysis also helps universities predict the number of students interested in a particular course.

According to education market share, skill assessment and behavior detection are the two major roles data analytics play in higher ed.

3) Optimizing the use of resources

Analytics offer appropriate data that helps enrollment officers, teachers, and other university resources enhance their operation. It helps in improving the quality of learning significantly while helping students overcome any issue they face during the process. Moreover, data analytics enhances the learning process by offering customized modules. These customized modules allow students to be more attentive and productive. Overall, institutions can enhance their resources because of the significant data availability.

Ways how data analytics can help universities

1) Improving administrative services

One of the effective ways in which data analytics helps universities is by improving administrative services. Whether it is ensuring a smoother operation in student retention or saving operational costs, analytics is said to benefit universities by at least 40% to 55% in this aspect. It has a real-time data feature around dynamic pricing that can help universities build performance modules. These performance modules further help minimize overall operational costs and improve services to a great extent.

2) Improving faculty performances

Yet another focus point of universities where data analytics can help is - improving faculty performances. One of the major concerns for higher ed faculty is creating a curriculum that students can resonate with effectively. With the help of analytics, faculties can develop and formulate a more efficient curriculum as it can analyze several factors like student engagement rates, quality of the course, and much more.

Conclusion

The higher education industry has evolved over the years and will do so in the future. Hence, it is imperative that universities keep up with the changing trends or needs of the students. Education backed by technology not only delivers the modern students' requirements but also helps develop effective strategies to meet institutional needs. Data analytics is one such technology that can help universities develop realistic yet favorable strategies for overcoming challenges like drops in enrollment rates. Leveraging this technology doesn't only help curb enrollment drops but also helps reduce operational costs and more.

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