AI Data Science Education Machine Learning

How Data Science is Improving The Education Sector

Data science is making the world smarter and efficient by making the prediction, understanding the impact and more. All the sectors are using it including education. In this post, we will be talking about the role of data science in education.

Data Science in Education

It helps us to extract the right amount of information from the data. And also build the strategic plan for the future time period. Here we will be talking about 5 use cases on how data science is being used in education. These 5 use cases of data science in education will help you understand how data science is being used here and what kind of problem we are solving.

As per the report by EdWeek, AI in education is one of the top priority for the investors-

Image Source: A report by EdWeek

5 Uses Cases of Data Science in Education

Although there can be many like the below 5 use cases of data science in education but we have picked top 5 for you!

Go through these and if you find any difficulty in understanding or need any technical help, please comment. Our team will share the required information/solution with you.

#1 Improve learning- Data Science in Education

Yes, what can be other than learning improvement in the use case of data science in the education industry. We can use multiple machine learning models and statistical techniques to help students improve the way they learn.

With the combination of big data analytics and data science, we can gather the data and process it to find many important factors. Some of those can be-

  • What are the areas students are lacking
  • What are their paint points like is it concepts, labs, or more
  • How timing can impact the adaptive learning
  • Maintaining the better environment in the premise
  • Improving the course content
  • Understanding the impact of professors on the students and more

You can gather the free datasets from any site and start leveraging those datasets to answer the above questions.

#2 Career Path Prediction

Machine learning can be used to predict the career path for the students. Along with it, it can also help in selecting the best colleges depending on the various factors.

In March 2019, Occidental College which is an art college in California, United States developed a model  which will consider each students’ college commitment decision. Based on the outcome of this model, the college chooses students matching the requirements. You can find more about the detailed article about it here.

Here are the steps involved in this model

Also, as per the official paper on it, the following algorithms were used to create this model-

  • Logistic Regression (LG) [Learn Logistic regression using Python]
  • Naive Bayes (NB)
  • Decision Trees (DT)
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (K-NN)
  • Random Forests (RF)
  • Gradient Boosting (GB)

The best thing about this model is, it is helping both college and students to get the best. A researcher Negithan White who is working on the joint projects by SupremeDissertations and IsAccurate says- only 10-15% of the students in the USA knows which path to choose after graduating from school.

This model solves this problem by analyzing their skill, objectives, skills, etc. And based on these, select the best path after graduating.

#3 Personalized Classroom

Although a lot has been done in the classroom personalization with the help of data science and machine learning. But a lot needs to be done further. And so, we have added classroom personalization as one of the use cases of data science in education.

A Boston, USA based startup, Brain Power has developed a machine learning model on the top of Google classroom which students with learning disabilities remain focused in the classroom.

The machine learning model analyzes the students’ engagement in the classroom and notify the teacher if any student needs any particular attention.

Brain Power is working further on it to achieve the goal where every student who needs personal attention can be automatically detected and served.

#4 Connecting the company with right set of influencers

Data science in education can be further generalized for the educational product company who want to reach a wider set of audiences.

For example, let’s say there is a company who wishes to reach a certain set of audience as a possible customer. For this, they might want to connect to some good influencers. Those can help the company reach the wider and potential set of customers.

There are multiple factors which identify the suitable influencers. For example, if the organization wants to reach out to the Instagram influencers, they might need to check multiple factors like number of followers, reach, engagement, niche and more. You can check more about such influencers in the fashion domain here.

Although just having more followers doesn’t guarantee for the best in the domain. There can be spammed followers as well. And here comes the role of machine learning algorithms which can detect such spam profiles. This way, the company can refrain from wasting the budget and time on such influencers.

In these cases, a spam detection machine learning algorithm can be helpful. Along with that, you can make use of other algorithms to get the right list of influencers. Even you can take help of influencers platforms as well which connect you to the right group of influencers.

For example, if you are a writer and want some influences to read your book, review it, and promote, you can take help of such platforms.

#5 Improve Grading System

Issue in the grading system is always a questionable topic. Many education professionals have raised questions on a biased grading system.

You might have found many scenarios when for the same student, teacher-1 has given A grade while another teacher might have given grade B.

Such kind of biasing happens as every teacher has different ways of grading and is the problem. This is the same problem which occurs when you will see one of the school’s students is getting a higher grade while other schools’ are a little lower. This is because of the nature of teachers who have graded the students.

To remove such biases, many schools are adopting the machine learning model to grade the student. This eliminates the risk of other biasing factors like academic performance, class attendance, etc. which usually teachers consider.

Some schools in China are adopting the paper-grading artificial intelligence (AI) in their classroom. It is estimated that one out of four schools are using AI for such kind of grading. And this has improved the grading system a lot.

Conclusion of Data Science in Education

This was all about use cases of data science in the education system which help the education system improve the performance. Apart from the above top 5 data science applications in education there can be many more use cases which you can choose and implement.

If you have implemented any data science application in the education system, please share with us in the comment.

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