Resource allocation within higher education institutions has become a challenge in an era affected by the COVID-19 pandemic and dwindling student enrollment numbers. These problems have affected schools’ ability to achieve their academic missions.
With the onset of big data reporting, stakeholders are devising ways to boost resource allocation effectiveness through technology. Data reporting enables organizations to leverage the information they collect for strategic decision-making.
Within data analytics and reporting procedures lies an opportunity to save resources for better allocation. This article discusses the enrollment challenge in higher ED. It demystifies how big data reporting can help reveal students’ learning styles, enhance enrollment within specific departments and co-curricular activities, and increase retention rates.
The Resource Allocation Challenge in Higher Education
The lack of access to sufficient resources has affected allocation and has become a significant challenge in higher education. Many educational institutions lack the financial capacity to stay afloat, leading to a crisis. There are many factors associated with the issue. Arguably the most fundamental problem has been the decline in enrollments.
As reported by Forbes, findings from the National Student Clearinghouse Research Center (NSCRC) indicate that the enrollment problem has been deteriorating in the post-pandemic era.
Surprisingly, there has been a year-on-year decline since 2020.
Postsecondary enrollment (undergraduate and graduate) has decreased by 4.1% within the past year. Moreover, higher education institutions have lost 1.3 million students in enrollment, representing a 7.4% decline over two years.
The ongoing recruitment crisis in colleges and universities cannot solely be attributed to the COVID-19 pandemic or low-income communities. There is more to it than that. For example, the interest in humanities has dropped significantly.
Many institutions have been removing Humanities options from their curricula due to their perceived non-applicability in the modern job market.
Most students argue that suitable employment in these fields is challenging unless they join university and college faculties. Such allegations have contributed to the dwindling university and college enrollments, forcing these institutions to seek alternative additions to their curricula.
Another problem has been the continued commoditization of degrees which have lowered their significance within the modern job market.
Degrees used to be highly valuable and were outright tickets to good jobs. When a job applicant had a college degree, employers used to be confident that they were hiring a competent recruit with the required skills for the job. However, this is no longer the reality today. Degrees do not guarantee job opportunities, regardless of the student’s GPA or alma mater.
The degree problem began when higher education institutions dug their heels into the degree model, which led to commoditization. There was more focus on skills and competencies than transmitting the foundational skills required for any career.
As a result, most students have been locked out of the modern job market.
It is now easier to acquire essential skills from apprenticeships, internships, massive open online courses (MOOCs), boot camps, and even social media. Newer institutions are also imparting skills that students can use across careers.
So, what solutions have been suggested, given the loss of revenues by higher education institutions?
One of the main focus areas has been the use of big data reporting to enact data-driven decisions. The use of data has been a common occurrence across multiple industries, and this process has led to the improvement of business-related outcomes. Let’s get to the basics of data reporting.
What is Data Reporting?
Although data existed long before the introduction of computers, technology has accelerated data creation. Technology has brought about the use of mobile phones, tablets, social media, the internet of things (IoT), and other data sources.
Educational organizations have had to leverage modern data management programs to streamline the collection, storage, and organization of this data before it can be turned into information.
Changing data into valuable information requires robust data analytics and reporting resources. Data analytics and reporting have helped professionals identify institutional challenges and relevant solutions, from educators to policy formulators and decision-makers.
The data reporting process enables organizations to leverage the information they collect for strategic decision-making. In a nutshell, a data report gathers information regarding varied issues and business aspects and then converts this data into comprehensible visual and graphical formats.
For example, a university could prepare a data report showing the income and expenses for the past financial year. An income graph can be included, depicting the total value of gifts, endowments, tuition and fees, grants, and athletics revenue. Professionals can then look at the data reports, gain insights, and make accurate conclusions and judgments.
An expenses graph can also be drawn, showing administrator salaries, building maintenance, supplies, field trip charges, etc. The data report can also comprise a written summary that explains the visual and numerical information contained therein.
The Benefits of Data Reporting in Higher Education
Data reporting ensures higher education institutions can access consistent records and use data to make decisions. Here are some advantages associated with big data analytics and reporting.
Make Data-Driven Decisions
Data reporting techniques help schools collect data for strategy formation and implementation. Using existing data allows institutions to tackle tough challenges such as declining enrollments. Big data can help stakeholders examine where the declines have happened, why, and how enrollments can be increased.
Data Reporting Is a Manageable Process
Big data is accessible and convenient.
Institutions used to leverage manual documentation and research techniques to collect data, which were time-consuming. Modern big data techniques are much more manageable. Schools just have to purchase relevant infrastructure and software to keep things running. The right tools can enable an institution to develop a more collaborative environment.
Data Reporting is Cost-effective
Data reporting also helps save costs in an era where enrollment numbers are dwindling. The process enhances resource allocation. For example, if a school has five Introduction to Politics classes, they can merge them to improve efficiency if 100% enrollment is not achieved.
This way, the institution can streamline the resources allocated for utilities and other related expenses. Another example is when cloud-based systems reduce data storage expenses. Therefore, data analytics and reporting are much more than a technological process since they can massively affect an institution’s finances.
The Types of Data That Educational Institutions Use
Before data analytics and reporting can be undertaken, a school needs to understand the types of data they can collect. The following are some of the main data types institutions can look at:
- Demographic data: Demographic data includes information that depicts the characteristics of a student or student population. For example, a school can look at enrollment, ethnic background, student mobility, the number of students with disabilities, attendance rates, economic status, behavior, and gender. Demographic data is vital because it tells the school more about learners’ social and economic characteristics.
- Achievement data: This type of data offers information regarding learner achievements and their learning patterns. Achievement data enables an institution to know more about students’ knowledge, skills, and competencies. Here, a school can look at standardized test scores, class grades, research papers, classroom-based assessments, and writing portfolios.
- Program data: Program data tells more about the programs that have been implemented within an educational institution. Examples are academic programs, professional development programs, and curricula. The purpose is to show whether they have been a success or not.
- Perception data: Finally, perception data examines stakeholders’ attitudes and beliefs regarding specific issues. For example, data can be collected regarding lecturers, staff members, students, facilities, school climate, academic standards, and school leadership. The purpose is to understand what people feel regarding specific issues and how the presence of challenges needs to be addressed.
The Use of Data Reporting to Transform Resource Allocation in Higher Education
As stated earlier in the article, the lack of access to sufficient resources has affected allocation and has become a significant challenge in higher education.
Educational organizations have been struggling with economic crises caused by declining enrollment numbers. Consequently, some stakeholders have turned to data reporting to aid decision-making. The following are some ways schools can improve student success centers and overall organizational success.
Using Big Data to Reveal Students’ Learning Styles
In psychology, one’s personality determines how one interacts with others and the world around them. This means that how students react to their environment affects how they learn things depending on their personality.
A student’s college success center can use a personality test to learn more about their students and what learning styles they employ. In this regard, the use of big data can be integrated into the personality testing processes to achieve better insights.
Personality test results can be coupled with important performance measures and social indicators to match students with the best learning environments. The aim is to improve student performance levels quickly so they can move on to the next stage of their syllabus.
Educators can also use the data reports to develop personalized educational plans and to recognize whether a student needs to learn online or within a traditional classroom setting.
On their part, learners can use the data reports to know more about potential career or educational paths. A student can look at the performance measures, social indicators, and personality test results to assess whether they should be in engineering or marketing.
In the long run, these processes can reduce the number of dropouts and help educational institutions attract and keep top talent since a school will be better placed to identify whether a student is likely to complete their program or not.
Enhancing Enrollment in Specific Departments and Cocurricular Activities
An educational institution can also use data reporting to identify students who are the best fit for specific programs. Take a university’s athletics program as an example.
Big data can be used to predict whether a student will become a successful athlete or not based on performance data. When an institution has access to this knowledge, they can know whether to allocate resources towards a particular student’s development or not.
Another applicability in this regard is with online programs.
Big data reporting can be used to understand which geographical locations require online recruitment and which one requires recruitment within the conventional campus setting. This applicability can be particularly beneficial to students and institutions because less money will be allocated for enrollment if it is done online.
Data collected from learners can also help schools know more about the preferred in-campus activities. The institutions can allocate more resources to departments with the highest interest.
Increasing Retention Rates
The final area in which big data reporting can aid resource allocation is streamlining curricula to boost student retention rates. It is now common knowledge that changes in the job market have changed learner perspectives, with most students now focused on finding alternative educational paths away from traditional academic institutions.
Schools can use performance data to streamline the test scores of non-performing students. For example, predictive reporting can show whether a student is likely to fail or pass over the subsequent two semesters.
A success center can use these reports to determine the likelihood that the student will drop out or complete their studies. If the former reality is identified, immediate measures can be enacted to boost the learner’s performance, thus, increasing institutional retention rates.
It’s evident that budgets are tight within higher education, and the proper allocation of these resources can make a significant difference in the school’s success.
Modern institutions can use complex analytic and predictive tools to generate vast amounts of data. The data collected can be used to determine the efficiency and success (or lack thereof) of programs, departments, and even students.
Some of the applications of big data within higher education that have been discussed are using the data to reveal students’ learning styles, enhance enrollment within specific departments and co-curricular activities, and increase retention rates.