Data Science in Education – The Modern Way of Learning [Case Study]
Data Science has spread its branches through several quintessential fields of the world today. It has emerged out as a global phenomenon that has revolutionized industries and has increased their performances substantially. As a matter of fact, Data Science has extended itself to incorporate education under its wing. Earlier we saw many Data Science Applications. Today, we will discuss the importance of data science in education. Also, we will see how it is transforming the learning with the help of a case study.
Stay updated with the latest technology trends while you're on the move - Join DataFlair's Telegram Channel
Data Science in Education
Education is the key to shaping the lives of people. It holds the power to transform and enrich the lives of people. Since the dawn of civilization, humans have evolved through education and have developed mechanisms to improve education. In the 21st century, where data is omnipresent in every walk of life, education is no exception. With advancements in computing techniques, it is possible to imbibe all the information through powerful big-data platforms. In this article, we will discuss how data science is playing a key role in making education better for society.
Applications of Data Science in Education
1. Social-Emotional Skills
Social-Emotional Skill is an important area that needs to be developed through education. Through this, a child learns to acquire a capacity to understand, analyze, express and manage emotions. He also learns how to develop a relationship with others. Facilitating growth in social-emotional skills is an important task of educational institutes. This is an example of a non-academic skill that plays a major role in defining the learning capabilities of the students.
Previously, there were various statistical surveys that would assess these social-emotional skills. However, with the advancements in computational methodologies, it is possible to gather a large amount of data. With the formalized knowledge discovery models in Data Science and Data Mining techniques, it is possible to gather such large information and incorporate it with the existing tools to produce better results. Furthermore, with the data that is extracted, data scientists can apply various predictive analytical techniques to help the teachers in understanding the motivation of the students for studying the course.
2. Monitoring Student Requirements
There are several evaluation and assessment techniques that are utilized by educational institutes. However, such traditional methodologies were often unable to capture and encapsulate all the important trends and patterns of student services. Furthermore, most of the assessment techniques were not in real time. With the advancements in Big Data analytics, it is now possible for the teachers to scrutinize their student requirements based on their performance and reviews.
As a result of monitoring student requirements, teachers are able to provide appropriate responses and even change their teaching methodologies to meet student expectations. Many times, teachers have an unconscious bias towards certain students. A data platform will treat its users with a zero bias, meaning that there will not be any bias in the evaluation of student performance. This will provide an equal platform for all the students to engage and develop their skills.
3. Innovating the Curriculum
Various Universities have to keep themselves updated with the demands of the industry so as to provide appropriate courses to their students. Furthermore, it is a challenge for the universities to keep up with the growth of industries. In order to accommodate this, Universities are using Data Science systems to analyze growing trends in the market.
Using various statistical measures and monitoring techniques, data science can be useful for analyzing the industrial patterns and help the course creators to imbibe useful topics. Furthermore, using predictive analytics, universities can analyze demands for new skill sets and curate courses that address them.
4. Measuring Instructor Performance
The performance of students depends on the teachers. While there are many assessment techniques that have been used to assess the performance of teachers, it has been mostly manual in nature. For example, student reviews about the performance of teachers make, have been the standard tool for quantifying teaching methodologies. However, all these techniques are not an efficient method and usually take time to assess. Furthermore, reading student reviews and creating an analogy is a tiresome task.
With the breakthrough in data science, it is possible to keep track of the teacher performance. This is not only valid for recorded data but also real-time data. As a result, with real-time monitoring of teachers, rigorous data collection is possible, along with its analysis. Furthermore, we can store and manage unstructured data like student reviews on a big data platform. And, with Natural Language Processing, it is possible to analyze the sentiments of the reviews and provide a comprehensive analysis of teacher performance.
Data Science Case Study
The University of Florida – Using Big Data Analytics to Mitigate Student Dropout
A college education is considered to be a gateway to a successful life. Many students enroll in colleges in the hope of making a career. However, many students drop out due to academic or financial challenges. According to a recent survey conducted in the United States, at least 54.8% of incoming students will be unable to finish their degrees within 6 years. This staggering percentage means that industries will have limited access to qualified individuals. This will also impede national growth.
In order to solve this menacing issue, education institutes are looking towards Data Science. One such Institute is the University of Florida. The University utilizes IBM InfoSphere to Extract, Load and Transfer data from multiple resources. It also makes use of the IBM SPSS Modeler for predictive analytics and data modeling. It aligns these two platforms with the powerful IBM Cognos Analytics.
IBM Cognos is a powerful, web-based business intelligence tool that provides various toolsets for reporting, analyzing and monitoring events through interactive visualizations. Using IBM Cognos Analytics, the university is able to analyze and predict student performances. It uses various variables like student background, demographics, high school grades, the economic background to assess the dropout probability for the students. Therefore, it will help the university in devising its policies and providing early intervention for students who are on the verge of dropping out.
In the end, we understand the diverse role of data science in the field of education. Data Science has revolutionized every sector and has brought a positive change to scenarios. We overviewed how data science is becoming an effective tool for shaping not only academic skills but also non-academic skills like social-emotional skills. Furthermore, it is used for evaluating the performance of students and teachers. Universities are using Data Science to curate curriculum, keeping in mind the growing trends in industries. Furthermore, we took a look at the use case of how the University of Florida is relying on the IBM Cognos Analytics to reduce the student drop out rate.
Excited to learn more about Data Science? Here is another interesting article – Data Science with Real Life Analogies. Please share your experience of this blog through comments.