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Education Trends 2022: Data Science in schools

  • Aileen Scott 
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Data Science is a growing field that has emerged in many key areas of our world. Data Science has become a global phenomenon and has significantly improved the performance of many industries.

Data Science has even incorporated education under its umbrella. Today we will be discussing the importance of data science for education & some ongoing education trends. We will also look at a case study to see how data science changes the learning process.

Data Science in Education

Education is key to changing lives. Education can enrich and transform lives. Humankind has evolved since the dawn of civilization through education and has continuously developed methods to improve our education.

Education is an integral part of the 21st century. Data is everywhere. Thanks to advances in computing technology, it is possible to access all information using powerful big-data platforms. Edvantic provides a variety of data science and artificial Intelligence curricula for schools participating in the data science in schools initiative. Students can use this content at all levels, including middle and advanced high schoolers. Students learn more about technology and career options through data science. They also discover future job opportunities in the data science domain. Students can identify real-world applications and examples of Data Science through assignments and projects. As part of the edvantic data science in schools initiative curriculum children will use analytical and data science tools to create reports and presentations based on their findings.

This article will focus on how data science can help improve education for all.

Data Science Applications in Education

1. Social-emotional skills

Education is essential for developing social-emotional skills. This teaches children how to analyze, understand, express, manage and control emotions. The child also learns to build relationships with others.

Educational institutes have a crucial task in facilitating social-emotional growth. This skill is a non-academic one that can help students develops their learning abilities.

There were previously several statistical surveys that could assess these social-emotional abilities. It is now possible to collect large amounts of data thanks to advances in computational methods.

Combining large amounts of information with existing tools is possible using the formalized knowledge discovery models in Data Science or Data Mining techniques.

Data scientists can also use the extracted data to develop predictive analytics techniques that will help teachers understand students’ motivations to teach the course.

2. Monitoring student requirements

Educational institutes can use a variety of evaluation and assessment methods. These traditional methods could not always capture all the important patterns and trends in student services.

Many of the assessment methods were not available in real-time. The advancements in Big Data analytics allow teachers to analyze student needs based on performance and reviews.

Teachers can monitor student needs and adjust their teaching methods to accommodate student expectations. Teachers often have unconscious biases towards certain students.

Data platforms will have zero bias and will evaluate student performance without bias. This will ensure that all students have equal access to the platform and allow them to develop their skills.

3. Innovative Curriculum Design

Diverse Universities must keep up with industry demands to offer appropriate courses to students. It is also difficult for universities to keep up with the pace of the industry’s growth.

To accommodate this, universities use Data Science systems to analyze market trends.

Data science is a method that uses various monitoring and statistical methods to analyze industrial patterns. It can also help course creators learn useful topics.

4. Measuring instructor performance

Teachers are the most important factor in students’ performance. Although there are many methods to evaluate teachers’ performance, it is mostly manual.

Student reviews on teachers’ performance have been a standard way to quantify teaching methods.

All these methods are slow and inefficient. It can be tedious to read student reviews and create an analogy.

Data science has made it possible to track teacher performance. This applies not only to recorded data but also to real-time data. With real-time monitoring by teachers, it is possible to collect and analyze rigorous data.

We can also store and manage unstructured data on a big-data platform, such as student reviews. With Natural Language Processing, it’s possible to analyze reviews and give a complete analysis of teacher performance.

Data Science Case Study

University of Florida – Big Data Analytics Used to Reduce Student Dropout

A college education is considered a pathway to success. Many college students go to college in hopes of pursuing a career. Many students leave college because of financial or academic difficulties.

A recent study in the United States found that at least 54.8% will not be able to complete their degree within six years. This shocking percentage will limit the availability of qualified workers for certain industries. This will also hinder national growth.

Education institutes are turning to Data Science to address this problem. The University of Florida is one such institute. IBM InfoSphere is used by the University to extract, load, and transfer data from multiple sources.

The IBM SPSS Modeler is also used for data modeling and predictive analytics. These two platforms are aligned with IBM Cognos Analytics.

IBM Cognos provides powerful web-based business intelligence tools that monitor, report, and analyze events using interactive visualizations. The University can analyze and predict student performance using IBM Cognos Analytics.

It uses variables such as demographics, student background, and high school grades to determine the likelihood of students dropping out. It will assist the University in developing its policies and offering early intervention to students at risk of dropping out.


We have come to understand the many roles of data science within the education field. Data Science has transformed every sector and brought about positive changes in all situations.

We discussed how data science could be used to improve academic and non-academic skills, such as social-emotional skills. It is also used to evaluate the performance of teachers and students.

Universities use Data Science to curate their curriculum in response to industry trends. We also examined how the University of Florida relies on IBM Cognos Analytics to lower student dropout rates.