We are surrounded by data, they are anywhere and everywhere. Now, just recollect how many datasets your educational institution has. And try to gauge the percentage you actually use and take advantage of.

If you realize that the share of actively used data is negligibly small, we have bad news for you. Perhaps, you do so for a variety of reasons: piles of heterogeneous information that is hard to structure, absence of appropriate data management tools, visualization complexities, and so on.

The truth of the matter is that you miss plenty of opportunities that could have significantly alleviated your teachers’ workload, streamlined administrative operations, and meanwhile boosted student outcomes.

In this article, we’d like to showcase the chances you voluntarily lose when not using the full potential of data. Let’s discuss why analytics must be an integral part of any educational software and which specifics of data management exist.

Starting From a Broader Perspective. Why Data Analysis in Education Is on the Rise

First, let’s refer to the statistics provided by KBV Reseaarch, which says “The Global Big data analytics in education market size is expected to reach $47.82 billion by 2027, rising at a market growth of 20.79% CAGR during the forecast period.”

Global Big Data Analytics in Education Market Size

That’s impressive, right? As we can see from the graph, there was a pronounced impact on market growth during the pandemic when everybody went digital, including educational institutions. Moreover, during this period, the demand for online courses on such platforms as Udemy and Coursera increased dramatically, and the trend is not fading but still gaining momentum.

This fact clearly revealed that the return to traditional educational methods is truly improbable, and the entire system craved for transformations quite a long time ago. Thankfully, the majority of schools and colleges have gradually acknowledged this plain truth and started to reconsider their processes and approaches.

Data analytics in education is one of the most essential pieces of the puzzle. Using appropriate tools and adapting them to your needs, there appear a plenty of opportunities you may offer to students and teachers.

More personalized approach to learners, precise identification and elimination of knowledge gaps, motivation stimulation, and help in career guidance — these are major fortes of data analytics for schools and other educational institutions that we are going to consider in the following paragraphs in detail.

Specifics of Data Analytics in Education. Why the Approach Has Little In Common with Other Industries

Specifics of Data Analytics in Education

Before we switch to use cases that highlight the substantial benefits of data analytics in education, first, let’s figure out how it differs from other industries. To do that, let’s take a company that produces and sells something, say, electronic devices.

Why do they need analytics and which metrics do they use? Financial performance, marketing campaign results, and employees’ KPIs. Agree, that the metrics will hardly vary from company to company, although businesses may be different, data they are curious about will be more or less similar and there’s no need to reinvent the wheel.

Discover 6 Types of Data Analysis that Help Decision-Makers

Education radically differs from other businesses, where financial information prevails. Mainly because it has completely different aims, not related to making a profit. And here there are multiple metrics that are equally important and must be considered, and sometimes, they are non-obvious.

Sure thing, there are data lying on the surface, such as class attendance, average scores on academic performance, and more. Meanwhile, to obtain maximum benefits from analytics, we need to look deeper. And here’s where the shoe pinches: sometimes it’s not an easy task to highlight the needed metrics and drill them down. However, specific tools, especially in conjunction with artificial intelligence, can make a difference here.

For example, we want to help a student with career guidance. Estimate, will these superficial metrics mentioned above be enough to help them select the direction? Apparently not. Here, we need to consider additional indicators, such as participation in extracurricular activities, blind spots in these or that subject, greater propensity to humanitarian or exact sciences, and even a psycho personality. We’ve mentioned just several ones, however, the list can go on.

Use Cases. How Data Analytics Brings Use to All Parties of the Learning Process

Use Cases of Data Analytics in Education

Steps Towards Personalization

All students are different. Some don’t have problems with prolonged concentration and can watch a 2-hour training video without interruptions; some do. There are students who perceive information by hearing, but there are also such learners with excellent visual memory. Let alone the fact that each person has their own learning pace, and if it’s slower than others’, it doesn’t mean that this person is a bad learner.

As you see the amount of nuances that must be taken into account is literally immense, and each student requires a unique approach to reach the best academic results. But how can this be done without data analytics in education? The question is rhetorical.

Check out how we created a Mobile Learning Framework for Offline Educational Apps

Motivation Enhancement

We also have another thing to consider closely related to personalization — student motivation. Imagine, you have a class of 20 learners, and some of them have problems with mathematics. To your point of view, how motivated will they be if a teacher doesn’t pay any attention to the gaps they have and continues presenting educational material at a tempo of the majority of the class?

If a learner sees that they lag behind and are not able to keep the given pace, the enthusiasm may be short-lived. Of course, at school, a pupil doesn’t have an opportunity just to give up. But if we speak about a university or online courses where a student can just turn around and leave in an instant? Agree, both options are so-so; in the first case, a learner will study through the power, and in the second, they will not study at all.

Trends Forecasting

Say, you launched an online course, and you are interested in having the enrolled students finish it and obtain certificates. With advanced analytics, you may notice when a learner loses motivation and intends to give up.

By identifying such patterns with data analysis in education, you are enabled to take preventive measures to avert such a scenario. For example, if a student wants to leave because doesn’t handle the offered load, you can offer an adaptive program. Or suggest some other directions that intersect with the previously selected one.

There are a lot of strategies to follow; however, having noticed some triggers, you are empowered to act proactively, and it’s you who chooses how.

Academic Results Improvement

If you check one test submitted by a student, the result can hardly be illustrative. It can be written well or poorly, but it’s impossible to judge the student’s abilities by only one verification work. Let alone the identification of weak points and working them out to improve the results.

Comprehensive big data analytics in education can help you capture the full picture. On the basis of both historical and current data, you can have a better visibility of a student’s performance and identify the blind spots promptly. Knowing them, you can select a strategy to follow: offer additional educational materials, videos, or extra lessons with a teacher.

Moreover, if you notice that not only one single learner has similar problems, but even a group of them, there is a possibility to reconsider the educational program accordingly.

Supporting Students with Impairments

Here we have tight interception with the healthcare sphere. Not all students are perfectly healthy and may have some deviations, such as dyslexia or ADHD. Obviously, it’s impossible just to bail on such students and continue presenting the material in the usual way.

This is where comprehensive big data analytics in education combined with AI can make a difference. By analyzing test results and other data, AI-powered tools can identify unusual patterns and alert teachers to potential issues before they become noticeable. Here, you can elaborate on a specific program suitable for children with impairments and create a separate group of pupils with similar problems.

Keep in mind that this approach cannot replace professional diagnosis. However, it can assist teachers and parents by flagging potential health concerns and prompting timely intervention to prevent further complications.

Read about the Role of AI in Education and If It’s an Invaluable Tool or a Phantom Menace

Despite the Good. Why Educational Institutions Are Still Reluctant to Take Advantage of Data Analytics