Skip to main content

How to Predict Student Success Using Big Data

By 08/04/2021No Comments

Educational institutions continue to seek ways to enhance the academic process using big data and predictive analysis. This technology improves academic performance and provides a better campus experience, from enrollment until graduation. 

Teachers and educators use big data solutions to fine-tune the learning process. At the same time, students can rely on any cheap essay writing service to provide real-time assistance on tasks. These data-oriented solutions are the centerpiece of traditional and virtual learning.

This article overviews how to predict student success using big data and predictive analysis in modern academic institutions. Let’s dive in.

8 Ways Big Data Can Predict Student Success

Adopting big data solutions is financially beneficial to academic institutions because it provides comprehensive insights into student performance. 

Here are the ways big data can predict student success.

  1. Adaptive Learning

Big data initiatives provide a centralized dashboard to monitor all aspects of students’ individual and group progress. By analyzing this data, lecturers and tutors can identify learning gaps among students. This will enable them to recreate academic modules tailored to each student.

Similarly, predictive analysis can aid instructors in becoming more effective. Regular feedback also ensures that educators take prompt action and provide a personalized learning experience.

  1. Student Support Through Timely Interventions

One advantage of using big data is that it pinpoints student stressors and performance lapses. With these advanced tools, educators can configure triggers to highlight a decline in performance. 

Notable universities like Georgia State University use big data to determine students in need of counseling. By eliminating achievement gaps and utilizing a personalized approach, this university has boosted its graduate community over time. Besides, GSU introduced programs like the Panther Retention Grants to help disenfranchised students from low-income housing.

Other human intervention strategies include tracking how students use academic resources, campus WiFi, library, and other university facilities. 

  1. Managing Student Enrollment

The admission period is always a tough challenge because colleges delegate loads of human and financial resources to streamline the process. But with big data solutions, colleges can manage enrollment smoothly. 

AI-powered technology can curate applications and classification based on specified criteria. Advanced interactive solutions can also handle orientation for first-year students.

Most importantly, big data predictive models can identify promising admission candidates based on academic and extra-curricular achievements. 

  1. Identifying Invisible Student Patterns

Educators often work with multiple students at the same time. As a result, they might struggle to keep in touch with every scholar’s well-being. However, computer algorithms can help teachers interpret incoming data and identify high-need students unprotected by traditional safety nets.

Some of this ‘intangible’ data include:

  1. Performance decline;
  2. Unexplained absenteeism;
  3. Late submissions.

This complex matrix of factors can be tracked and interpreted using predictive algorithms. Essentially, universities need to implement these big data models in every faculty.

  1. Accurate Risk Assessment

Machine learning protocols are structured to analyze data and display bottlenecks in the system. This feature improves efficiency when dealing with multiple students. By implementing the obtained data, educators can generate a comprehensive picture of risks across the entire student population. 

Essentially, they can come up with a cost-effective solution that addresses these bottlenecks. For instance, big data systems can display a decrease in student engagement, which teachers can address by providing captivating learning material.

  1. Peer Engagement

Big data provides real-time, granular data streams showing interactions between students. The culmination of these behavioral data sources generates better models focused on assessing different risk dimensions. 

Similarly, data from student-teacher and teacher-teacher interactions should also fall under this analytical framework. This centralized system fosters a wholesome and transparent learning environment. 

  1. Versatile Options

Big data doesn’t simply identify student weaknesses or problem areas; it guides the students on what course options suit their strengths. School administrators can also redirect struggling students to alternative career paths.

But that’s not all; students can explore other career alternatives regardless of teachers’ intervention. Since they have access to performance data, they can decide when to change majors to a different specialization.

Elite universities might not need big data technologies to reduce drop-out rates. But middle and lower-tier schools need these advanced frameworks to boost student’s chances of making it through college.

  1. Monitoring Trends

Student data gathered over time often gets lost in physical storage. But with the advent of cloud technologies, big data initiatives can provide administrators with organized and relevant historical information.

This data predicts emerging trends and changes in student behavior. These predictions will then help to facilitate learning strategies profitable to student success. Besides, keeping in touch with trends also helps in counseling by providing the advisor with real-world data.


Global digitalization has shown us that it’s beneficial for educational institutions to use big data to improve student retention and graduation rates. Although most big data initiatives are used for administrative decisions, teachers can use them to personalize learning materials.

Also, big data improves enrollment by identifying the best candidates with promising futures in a given field. At the same time, institutions can use these AI-powered tools to assist disenfranchised scholars.

Ultimately, big data places students at the center of the academic process. By combining the human factor with analytics, institutions can predict and guarantee success across the board.