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  • Helen Kyianovska

Predictive analytics in education: use cases, best practices, and perspectives


The use of predictive analytics in education is becoming a hot topic. A growing number of educational institutions are looking for data analytics tools to improve their revenues and optimize the use of their resources. By 2027 the size of the education and learning analytics market is expected to reach $34.7 billion.


Predictive analytics refers to a range of statistical techniques, allowing us to predict unknown events based on historical data. Educational institutions and organizations can use information about academic records, enrollment, attendance, class engagement, etc.


Why is predictive analytics important for the education industry?

How can it be used in this sector? What should you pay attention to while implementing such solutions? How will predictive analytics change education soon? Read on to find out!



The importance of predictive analytics in education


Education faces both new challenges and opportunities. Colleges and universities shift their focus from enrollment statistics to completion rates. It is especially evident in the United States, where only 74% of college students returned for their second year of education in the fall of 2020.


Retention rate is especially important for a higher education institution as it influences both revenue and expenses. Students who drop out without graduating won’t pay their tuition fees any longer. Thus, customer lifetime value (LTV) drops. The university has to spend more money looking for new students. That’s why identifying potential dropouts as early as possible and helping them to succeed is of prime importance. This is where predictive analytics steps in.


In the USA, overall persistence rate (the share of students who get back to a college for the second year) has remained around 75% for the last ten years.

Schools, colleges, and universities have always accumulated historical data to predict student success. Attendance records, examination results, and so on were collated and analyzed manually. The information was often siloed within departments. It took the institution some time to see the bigger picture and take some actions.


At present, educational technology and digital learning provide tons of data. Staff and management could use this information to boost students’ performance and data-driven decision-making has the potential to improve financial results. However, to mine those useful insights, the right analytical tools are required. Predictive analytics can help pre-schools, higher schools, and universities to improve their performance.


“Predictive analytics has evolved into a hot button topic among educators in order to better serve students by becoming more data-informed. This is a result of the intense pressure placed on universities to demonstrate an ROI for students as the U.S. dropout rate continues to be at an all-time high”, says Brian Rowe, founder & CEO of Perceivant.


Let’s find out how to use predictive analytics in education.



Predictive analytics use cases in education


The main steps of implementing predictive analytics in education

New technologies and more data provide numerous opportunities to utilize predictive analytics in this sector. Here are some of them.



Enhanced student advising


So-called Flags or Early Alert Systems can spot students likely to fall below usual attendance levels. Algorithms can produce forecasts starting from the first semester. Poor performance in some introductory courses may also predict a higher risk of failure. Thus, advisors are warned well in advance and can intervene, offering students their support. In the long run, educational organizations get more successful and satisfied graduates, improving their reputation and lowering customer acquisition costs for new students.


Georgia State University, in the United States, provides one of the examples of predictive analytics in education. They hired a company to analyze 140,000 student records, looking for predictors of dropouts. The researchers found 800 “marker courses.” The success in those courses taken early helps to predict whether a student can cope with more challenging courses later.


Let’s take the nursing program as an example. The researchers found that most graduates had a B or better in introductory English, taken in the freshman year. In other words, the statistical analysis showed that success in this course is a good predictor of completing the program. Students who get less than B should be watched closely, as they are at higher risk of dropping out.


“Predictive analytics can be a particularly powerful tool for CIOs in higher education. Skeptics might claim the outcomes of predictive analytics — such as identifying a potential student dropout — could have been otherwise determined, but their real power comes from the way these analytics systems socialize the prediction at hand among a range of stakeholders to remedy the issue at hand”, points out Glenda Morgan, senior research director at Gartner.



Personalized learning


One more way of how predictive analytics can improve learning is by identifying crucial learning gaps that are likely to affect the students’ success. Analytical instruments can be used to process academic records and provide teachers and professors with such insights. Thus, educators can customize academic modules to tailor them to the learning patterns of various students. As a result, the latter have more chances to continue their studies, generating more revenue for educational institutions.



Resource optimization


Colleges and schools using predictive analytics systems can look into the attendance statistics and academic records for different classes and teachers’ (professors’) workload to find some trends. It can help them to allocate their resources better by optimizing curriculums. Thus, administrations can manage expenses (salaries of the teaching staff, utility bills, etc.) most efficiently.



How to implement predictive analytics for education: best practices



Implementing predictive analytics in the classroom is a complex process. However, there is a general guideline you can use to make it successful:


  • Identify bottlenecks in your operations, and define metrics for data analytics to focus on


  • Find data sources with necessary information


  • Digitize the techniques used for improving students’ performance


  • Use statistical analysis to find some insights


  • Automate data collection and analysis, design a mechanism of receiving notifications with results of this analysis, and guidelines about acting on this information


Various things may go wrong while designing and integrating this new tool into your business operations. Pay attention to the best practices below, they will help you to cope with these challenges.



Allow students to control their data


The efforts to bring predictive analytics in the education industry may suffer from concerns about privacy violations. That’s why gathering data should be as transparent as possible. The students should be able to opt in or out. Be clear about how data is used and maintained, and explain the benefits of opting in to get their consent.


Note that you can avoid personally identifiable information altogether. For example, some universities track class-wide data to learn how students utilize on-campus services.



Ensure data protection


If an educational institution collects large datasets of students’ personal information, it is responsible for its safety. Boosting cybersecurity should come hand-in-hand with using predictive analytics to improve student education. Otherwise, the data may be compromised as a result of a cyberattack.


Besides, educational institutions should impose strict data governance rules. Few staff members need to access the full scope of student data. For instance, some employees may require just attendance statistics, while metrics on the library service usage are essential for others.



Check algorithms for implicit biases


Algorithms are often expected to provide impartial judgments. Nonetheless, their creators might bring some biases into the picture. They can do it by using skewed data to train the model. Thus, an algorithm can even exacerbate the influence of a structural bias.


Including such identifiers such as secondary school, ethnicity, or postal code in the predictive model can arguably produce a distorted picture of students’ needs. For instance, Georgia State University, mentioned above, purposely excludes these factors from its algorithms.



The future of predictive analytics in education


The education sector is evolving, and predictive analytics in EdTech is part of that evolution. Let’s have a look at several trends, which will influence the industry in the near future:


  • Predictive analytics will boost the advance of personalized learning. Education moves from a one-size-fits-all strategy to precision education. The former was designed for an average student, whereas the latter considers the peculiarities of individual learners and their environments.


The analysis of learning patterns allows teachers and professors to predict students’ performance and optimize the learning process by prompt interventions. As a result, personalized education will improve learning outcomes, boosting satisfaction from the process both for students and educators.


In the next few years, the demand for agile learning systems and adaptive educational tools will grow to accommodate the pace and progress of individual learners. Besides, such tools will have to meet the needs of specific groups of students, such as ones with learning disabilities. Individualized education will be able to adapt to individual needs almost in real-time.


  • COVID-19 will speed up the implementation of data analytics in higher education. The pandemic demonstrated that digital education systems are highly flexible. Such instruments, including predictive analytics models, can be robust enough to shift to remote education quickly. Executives and administrators can get data for higher-level decision-making while the teachers get insights into supporting individual students.


The COVID-19 didn’t change the education sector overnight. Still, it brought online learning and digital tools into focus. Thus, the building blocks for a new model of education are becoming more evident. Pre-schools, schools, colleges, and universities are getting more interested in data analytics systems, allowing those institutions to use their resources more efficiently and produce better learning outcomes.


  • Predictive analytics will be implemented with a human touch in mind. The algorithms will become even more refined. However, they alone aren’t enough to reap all the benefits of analytics. The success also lies in the response of administrations to the insights mined from the data.


As the universities amend their student support strategies, the impact of predictive analytics in higher education will include higher student retention rates. In other words, configuring an algorithm to send an automated message “you’ve earned a low grade in …, you should do...” won’t solve the problem. The university should also ‘configure’ its campus culture, encouraging staff to reach out to students at risk of dropout and proactively connect them with the support systems.



Why should you choose Menklab to build predictive analytics solutions for education?


Menklab has been creating remarkable web, mobile, and software applications since 2013. Our portfolio includes complex solutions for the education industry, employing predictive analytics. They can help educational institutions to use their resources more efficiently, doing more with less. To understand how it works, let’s have a look at a vivid example.


Each school or preschool has a fluctuating amount of pupils in their classrooms throughout the day. Thus, the demand for teachers and classrooms also changes. Educational institutions, using traditional methods to manage their staff and utilities, might be slow to react.


Our software can analyze the historical data and predict those fluctuations. Iit can also calculate the required number of teachers and classrooms at each moment and help administrators to manage the teaching staff. As a result, the expenses for wages and the physical plant are kept under control.



Implement predictive analytics in education with Menklab

Predictive analytics systems offer plenty of opportunities for educational institutions. They can forecast students’ outcomes, spotting those at higher risk of dropout. Thus, universities know well in advance which students need help. Teachers could use a custom learning program to boost their engagement. As a result, higher education institutions can increase their retention rates, student satisfaction and consequently revenues.


Analyzing attendance statistics and academic records can be instrumental for curriculum optimization. On top of it, predictive analytics allows to streamline operations and cut expenses.


Such tools will become more and more popular as educational institutions try to keep up with modern tendencies.


If you’d like to create a new software solution for the education industry using predictive analytics, Menklab can become your reliable partner.


We have experience in this sector as well as expertise to create the necessary solutions and sophisticated algorithms. Our engineers use development best practices and employ rigorous testing to control the quality. We strive to build honest and transparent relations with our clients.


Contact us — and we will help you to revolutionize EdTech.

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