Can Data Make Each One of 40,000 Students Feel Special?

“Big data analytics promise innovation and transformation across endless disciplines; its proponents argue that it will disrupt the Blackbox of the past and send us into an enlightened, all-knowing future,” wrote Ben Williamson in his book, Big Data in Education: The Digital Future of Learning, Policy and Practice.

Indeed, we’re now witnessing but a glimpse of the transforming potential of data. Let’s explore the recent developments within the higher education sector to seek the truth in Williamson’s remark.

Universities have long collected and used data in many forms. Starting with accepting applications from thousands of students taking entrance examinations, universities are instantly subjected to a vast amount of data. Then, they have to filter those who are eligible to enter the university.

But look closer and you’ll see a problem. Students are commonly required to fill out numerous forms that ask for their personal information; to apply to a particular college, then in a particular department, when applying to their desired degrees, their preferred courses, clubs, or organisations.

It goes on and on throughout the duration of their studies.

This hierarchical procedure of processing data creates data silos, leaving the data of the students scattered and unconnected. As for the universities, this problem may affect them in the long run, because some vital data may be overlooked and not used in analysing the students. They end up with a typical scenario where they sit on all this valuable, but unused information, before it is eventually expunged.

If this problem sounds familiar, it is one that is faced by organisations across many industries. IDC estimates that 90% of digital data is unstructured, and 90% of that consists of “dark data” that is never analysed. So, you do the maths.

The Data is Already There – Time to Make It Work for You
With this in mind, a good platform that gathers, analyses and predicts data should be used to see the bigger picture. For universities, it is not only about keeping tabs on the data the students have shared, but also using their performance inside and outside class to give insights on their most effective learning methods.

In the case of Florida State University (FSU), its Office of Distance Learning (ODL) found a way to use data and predictive analytics for over 40,000 students. They use this technique to enrich and improve the students’ learning based on their background, behavioural data, exam results, schedules, and other aspects whether outside or inside the lecture halls.

Cloudera enabled ODL to utilise the university’s Learning Management System (LMS) more efficiently and effectively. FSU uses Canvas LMS to track student activities in a particular course and with Cloudera’s data analytics tools, ODL can look into patterns and use it to identify students who are at risk of failing.

Also, Cloudera’s machine learning capability, using algorithms such as Logistic Regression in Apache Spark ML, can predict whether a student will pass a course or not. Another use of the Cloudera platform is collecting the data from the students, allowing full access for the ODL, which ultimately avoids data silos.

What does all this translate to? Deep personalisation and adaptive, data-driven learning to benefit both students and universities.

Cloudera Data Platform has given FSU a 360-degree data-directed view of their 40,000 students, giving them the ability to customise and personalise the learning methods for each and every one of them. With data and predictive analytics from the Cloudera platform, every student is special and looked after leaving no one behind.

FSU now finds itself on the right path towards an enlightened, all-knowing future. To find out more, click here.

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