Data analytics can help revive traditional higher education

Economists agree that education is one of the primary drivers of economic development. An economy blessed with intellectual minds will certainly achieve higher and more sustainable economic growth than its counterparts.

That’s why from ancient times right through to medieval times, many of the leading civilisations prioritised and promoted the development of scholars by building cutting-edge educational institutions (at least for that period) like the University of Ancient Taxila, Cambridge, and Oxford—some of which still stand today.

Since these ancient universities, higher education has grown significantly, driving advances in technology, literature, science, architecture, and more along the way.

That said, with the introduction of the internet, higher education has become more accessible. Modern students have access to unconventional academic avenues to further their knowledge.

Today, traditional higher education is at a critical juncture. With the rise of web-based learning, students are more likely to drop out of traditional higher education systems to pursue education via more convenient and sometimes more insightful channels.

In recent years, the higher education dropout rate has increased steadily. In the USA, almost 40% of students from each academic year are dropping out of colleges and other higher education institutions.

All this begs the question: How can traditional higher education institutions stay relevant in this digital boom? Big data analytics might hold the answer to this question.

In this post, let’s explore the role of data analytics in modernising higher education.

Why is traditional higher education a less obvious choice now?

Before diving into what traditional higher educational institutions can do to remain competitive and relevant, it is important to understand why they are becoming a less obvious choice for modern students.

According to a study, higher education remains inaccessible for many students due to high costs. In fact, in the USA, an average student has a debt of $32,731 when they complete their higher education through the traditional route. 

Compared to this, non-traditional channels are more affordable for students of almost all backgrounds.

Other reasons for the loss of popularity of traditional education include the lack of integration of modern learning and teaching tools, out-of-date curriculums, and a lack of real-world skill development.  

How can data analytics help traditional higher education?

• It can help develop relevant and personalised curriculums

Universities and other traditional higher education institutions sometimes persist with a tried and tested curriculum for an extended period. While this can help standardise the teaching, learning, and testing process, it doesn’t educate students about the current trends, latest breakthroughs, and empirical findings.

With data analytics, these institutions can identify the most relevant topics to include to create an up-to-date curriculum.

Also, many students benefit from having a personalised study plan that caters to their learning requirements. Data analytics can help educators identify and track the progress of each student in a detailed manner, which helps them personalise the curriculum to make it more comprehensible.

In addition, data analytics can power a more up-to-date skills development programme for students by identifying the skills in demand and providing educators with insights to help improve the existing skills training systems.

• It can help match students with the right course

Many students face a conundrum when deciding what course to pursue in higher education, and a considerable amount of students choose wrong—a leading cause of increasing dropout rates.

With data analytics, higher education institutions can build a recommendation system that considers students’ competencies, skills, interests, and career ambitions and recommends the most suitable course for them.

•  Data analytics can help reduce the cost of education

Data analytics can help higher education institutions identify cost-cutting opportunities. While not directly related to the curriculum or other academic activities, this can help these institutions reduce their administrative costs. Universities and other institutions can transfer these savings to reduce tuition fees for students.

This way, more students will have access to higher education while academic institutions will attract more students and increase their revenue.

The University of North Texas, for example, leveraged the power of data analytics to reduce administrative costs by $1 million and transferred the savings to increase their student retention rates.

Data analytics might be the need of the hour for higher education

With increasing competition from non-traditional education channels, traditional higher education is becoming less popular among students, but data analytics can change that.

By integrating advanced analytics, higher education institutions can stay relevant and regain the lost popularity.

How can big data analytics help improve the education sector?

With each passing year, the amount of data created on online platforms has increased astronomically. All of this information is crucial, as it allows businesses to understand the myriad of intricacies that exist in today’s markets. With more and more companies going international, the competition is fierce – and it’s only going to get tougher as the new decade rolls on. Similarly, with so many options available to them, consumers are pickier than ever, and gaining their trust and attention is paramount to making sales and conversions. It’s no surprise then that most organisations have begun to adopt big data analytics into their business process.

Big data systems are able to collect, store and process vast chunks of user data and make sense of them. Essentially, it translates all of this unstructured data into valuable insights that can be used in your everyday business processes. Now, it may seem that the utility of big data is mostly confined to business-consumer transactions; that the ultimate goal is driving conversion. In reality, the potential uses for big data analytics are far greater – it can play an integral role in the advancement of various industries and sectors. One of the best examples to illustrate this is the education sector.

Future insights can play a vital role in an academic setting and big data analytics can prove to be an amazing tool that can help improve the processes of teaching and learning. Here’s how.

Creating customised curriculums with big data analytics

Good curricula serve as the backbone of any education model, and designing one can often prove to be a challenging task, requiring extensive analysis and expert supervision. Still, no matter how inclusive and intricate a curriculum will try to be, it simply won’t be ideal for everyone that’s taking it up. Though it may seem impossible, it is possible to create customised curricula on an individual level through a combination of big data and online learning programs. So, how exactly can this be done?

Users these days are connected to a wide variety of platforms and devices, from smartphones and smartwatches to social media sites and online forums, and these are an excellent source of information for educators. Based on the big data analytics derived from all these platforms, you can create educational courses that better fit the needs and preferences of your student base.

Traditional classrooms stick to static courses with no real room for flexibility – everyone is expected to follow the same set of steps. The more streamlined and personalised curriculums that big data analytics allow for are a great start, but there’s no reason to stop there. You can take things a step further with big data systems by way of online learning classes. Here, you can allow students to do their own self-learning while you pinpoint target areas best suited for them.

Increase student performance and reduce dropouts

Increasing student performance and providing an effective, stress-free learning experience should be the primary objective for contemporary educators. In order to achieve this, it’s paramount that they identify what the problem areas are for students, understand what their learning difficulties might be and correctly predict which students might be struggling and thinking of dropping out.

Implementing online learning methods, as we mentioned earlier, is one way to achieve this. But remember that you can keep track of this data as well. With big data analytics, you can easily visualise what subjects or areas your students prefer to self-learn. You can set up self-assessments that will store all the scores your students achieve, and through that, you will be able to identify which students might need additional help.

Career prediction via big data analytics

Accessing student performances, interests and strengths – especially over the course of their academic life – can provide a clear understanding of what their ideal career paths should be. This information can easily be relayed to students by educators; they could point out these patterns to undecided individuals and help them reach a decision.

What’s more, you can then begin collecting data on students who went through your education system and joined the workforce. Here, you can assess how effective their career choice was, and use it to optimise your curricula and make even better decisions in the future.

The future of education with big data analytics

The rapid rate at which organisations have adopted big data in order to provide better experiences for their consumers has transformed the way markets look at data. The amount of raw data available to be processed is quite astronomical, and there is immense potential in the insights you can derive from big data analytics.

As far as the education sector is concerned, efficient utilisation of big data can lead to a great many improvements – for both students and educators. And even though it may take a while for education systems around the world to unlock the true potential of everything big data has to offer, the future certainly looks bright.