Big Data in education can connect students, teachers, curriculum and content to create a personalized learning experience and prepare students for industry demands. It provides instant feedback to students and helps them improve through optimized resources and reduced errors. Data mining and analytics extract information to develop strategies to address achievement gaps and help teachers and administrators make smarter decisions to meet student needs. While big data risks privacy and limited human intervention, it has also helped reform education through massive open online courses and is estimated to become an $8.5 billion industry in the next five years focused on fields like humanities and computer programming. Predictive analytics can predict student outcomes like likelihood of success with 96% accuracy compared to 82% for external consultants.

1. Big Data in Education - An insight into how technology can influence the learning process
By Sriraman Rajagopalan
Education is a medium of learning involving teachers and students where teachers impart
knowledge and learners grasp the concepts. <tense> This was followed by question-answer
sessions, quizzes and tests to test a pupil’s understanding.
With the introduction of technology, to be more specific, ‘Big Data’, the process of learning is
changing across the globe.
Broadly, ‘Big Data in education can be used to connect the contents and objectives involving
students and teachers, connect curriculum, disciplinary intelligence of teachers, content and
delivery to help create a learners’ experience and create an environment to integrate skills and
knowledge to meet industry demands.
The biggest advantage of having an automated system is the instant feedback provided to the
student. In a traditional learning process, this is extremely difficult because it requires a lot of time
and effort to evaluating and grade, leaving no time to provide feedback to students. This new
system also helps students to improve their learning by proactively showing new steps, optimize
the utilization of digital resources and reduce chances of errors during evaluation. The students’
reports can also be used as a useful tool to improve research, develop better evaluation
techniques, bringing in greater accountability and introducing innovative learning approaches.
The above utilities can be achieved with effective use of Data Mining and Data Analytics – an
integral part of Big Data. Data mining provides tools and utilities to extract and analyze data to
develop strategies to minimize gaps in achievement. Data Analytics techniques enable to make
smarter decisions (by teachers and administrators) to understand and cater to (the students’)
needs.
Using data analytics, the success of a student can be highly optimized by generating specific types
of reports that focuses on various trends like ‘why’, ‘what’, ‘how often’ etc.
Risks of Big Data in education include Privacy, Limited human intervention, Deceiving numbers
and tall claims. Some typical challenges include data ownership, infrastructure for institutes to
store data, costs for massive Research and Development, and d
ata security.
Big Data has helped to reform the educational delivery by creating MOOC (Massive Open Online
Courses) that have allowed large quantities of information to be shared via the Internet. Coursera,
Khan Academy are some of the well known online institutes.
Big Data in Education is an estimated $1.3 Billion business that is expected to go up to $8.5 Billion
in the next five years – which is about 36% compounded growth rate. The area of focus is in the
field of humanities, management studies, computer programming, education and training, and
other consulting services.
1 / 2

2. To conclude, Predictive Analytics can predict student outcomes like likelihood to succeed, finish
school etc. Survey indicates a 96% of accuracy in the Predictive model compared to 82% success
achieved by external consultants. Though it is in its infancy, Predictive Analysis is likely to emerge
and evolve into a fully developed model in the next few years by utilizing every aspect of data and
it’s forms.
2 / 2