How Data Science and Machine Learning is revolutionizing the education sector.

Big Data, Machine Learning and Data Science has affected each and every field and Education hasn’t remained untouched. Although, the extent to which it has transformed Education sector is less right now but the future looks promising!

The main objective of using Data Science and Machine Learning in education is to make learning more personalized, so that both the student and the teacher know exactly what needs to be done to improve the quality of education. But, for doing this, data is required. Data of every activity of every student on what is their approach to learn something. Now, this data cannot be taken manually. This is where Online Learning comes into the picture.

While we are learning something online, we leave digital footprints. Digital Footprints means a track of every activity of a user inside a Virtual learning environment. Some of the digital footprints are how often one logs in to the system, how much time they spend on videos they watch, how much time they take to complete their assignment, what links they click etc.

The main source of data being used for learning analytics is the virtual learning environment (VLE), where students increasingly have to go to view timetables, assessments and course information, access learning materials, interact with others through forums, and submit assignments. The use of the VLE varies considerably across modules, courses and institutions. When it is used as the primary means of course delivery in for example distance learning programmes or MOOCs it is likely to provide a rich source of data.

The second major data source is the student information system (SIS), which contains data about students such as their prior qualifications, socio-economic status, ethnic group, module selections and grades obtained to date. In some institutions attendance monitoring systems are in place which record campus visits by students or their presence in particular locations such as lecture halls, libraries and refectories. This can be captured from swipe cards, proximity cards and other entry systems, or from accesses to institutional wifi services by students.

Information such as a students’ library visits, records of book borrowing and access to electronic journals is provided to personal tutors in some institutions on dashboards for facilitating conversations with individuals or groups. For instance, analytics of historical data for a module may show that success is associated with frequent access to library resources. If analytics on the current cohort show that fewer students than expected have so far visited the library, an instructor can bring this to the group’s attention, with the intention of modifying the students’ behaviour.

This is just one simple case of the various things that can be done from Data Analytics in the education sector. It could prove fruitful for the teacher and make their task easier. This is because when you know exactly where the student is lagging, you can fix the problem and help them learn better.

Many Universities across the world are developing systems by using Data Analytics and Machine Learning for helping their student to score good grades and improving their ability to learn quickly and efficiently. Here are some cases where Analytics has transformed education in the University.

While this technique provides solution to almost all the problems related to learning, one of it's dependencies is the need of data to be online. For that, the whole education system must shift to an online methodology. Now, this is big hurdle in the path of learning analytics to prosper because we cannot do this all of a sudden. The transition should be gradual.

I personally feel that by the end of this decade we’ll have almost half the Universities of the world using Data Science and Analytics to help the students on how efficiently they can study and the teachers on how efficiently they can teach.