Data analytics has proven its worth in a variety of fields. It’s also useful for tracking students’ progress and assisting teachers in the classroom in implementing an adaptive teaching approach for their pupils.
Since the beginning of time, teachers have always been involved in data collection. They have been linked to data since the dawn of complicated technology, from collecting marks and attendance to analyzing every student on result day.
Data Analytics In Education
Teachers in many organizations, schools, and universities are faced with unmanageable student-related data. Each kid has their own set of characteristics and learning skills, as well as a diverse variety of socioeconomic origins. As one of the primary pillars in constructing study plans, the instructor must also figure out what data must be collected for a certain teaching policy.
The world’s educational system is extremely skewed. When each student has a varied learning capability, it has a similar framework for all types of students in the class. Everyone is subjected to a curriculum designed for a specific group, yet not every student has the same ability to learn. The educational system is not tailored to each student’s unique abilities and needs.
Teachers may be enabled to do much more than they do now to access children and make the most of their chance as educators, thanks to advances in data analytics and technology.
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In the Education Sector, How Can Data Analytics Be Used?
Statistical Models:
They can be used to predict students’ grades in a class. If the model predicts that the student will have a low CGPA based on the data collected, the model can send an alert to the teacher, suggesting that the student will need to work harder to get the required grade. The teacher may determine which topic a student is weak in and create study programs for him appropriately.
Judging Panels:
The judging panel can model the student’s performance on the entry test to see if he or she has the potential during student interviews to schools and universities because these admission tests and CGPAs have a very strong correlation. They may also keep track of absence rates and use models to see how this impacts the performance of all of these pupils.
Dropout Rate:
Many students in various regions of the world drop out of high schools and universities for a variety of reasons. Predictive models aid in assessing the risks of student dropouts through data analysis and, as a result, in implementing preventative steps.
Virtual Interview:
There’s also an AI-powered virtual interview platform that looks and feels like a real face-to-face interview. It may be used to assess the candidate’s body language automatically. In the classroom, the same methodology may be used to determine who is paying attention, who is not, and who is pretending.
Cloud:
Teachers may make use of cloud technology to provide pupils the most access to study materials possible. Technology may also support autonomous learning, allowing students to take control of their education and learn even when they are not in school. The University of Michigan employs a program called E2Coach to provide individualized course performance notifications to its students based on a constantly updated algorithm.
Let’s take a look at how Indian Ed-Tech companies have harnessed the power of data.
Udemy
By monitoring learner search data throughout the site to uncover trending themes and new areas where learners are seeking further courses, Udemy employs data analytics to alert instructors of emerging themes where learners may be seeking more information.
The firm has also used sophisticated matching and suggestion algorithms, as well as ratings and reviews, to guarantee that learners receive the appropriate information from the appropriate teacher at the right time. Udemy analyses billions of learner data points regarding preferences and efficacy using machine learning and data science to provide the broadest range of in-demand, new, and engaging courses to people and organizations.
Testbook
The platform employs machine learning and data analytics to propose the most relevant competitive examinations to every student based on his current level of topic comprehension. The firm is currently attempting to expand this to include skills and private employment possibilities, where it will match its calibrated students to the appropriate possibilities based on the employment requirements and the student’s current knowledge level.
BYJU’s
BYJU’s data is divided into two categories: quantitative and qualitative data. When it combines its research and student input into the product design process, it analyses qualitative metrics through the product development phases. After the product has been released, the firm will investigate quantitative measures. This is how it keeps track of how satisfied students are with its product offers on a much wider scale.
The downside of Classroom analytics
Although an early projection of a student’s scorecard may be beneficial in choosing whether or not to enroll the student, making a final decision solely on this would be unjust. A variety of things can influence a student’s grade, either negatively or positively.
If a student is motivated and dedicated after being accepted, it is unjust to condemn him only based on a bad performance forecast created upon his admission using a statistical model. On the other side, a student whose model predicts a great CGPA may be unable to study as hard or may experience other challenges and life events that alter his curriculum.
Data analytics will be 100% effective in analyzing student performance in classrooms if it is used for all the correct reasons. It’s a terrific tool for teachers to evaluate pupils so that those who need early intervention may get extra help, lowering the student retention rate. In this solution, data analytics offers a lot of promise to increase student accomplishment, provide pupils a competitive edge, and empower teachers to be the best educators they can be.
Wrapping up
To a large extent, ed-tech has democratized education. The importance of online learning and ed-tech has grown rapidly as a result of the COVID-19 wave. The future of education is already changing, with more flexible positions arising that will benefit learning.
Data has undoubtedly played the most important role in this field; everything has been impacted by big data, from online algorithms that are used to construct specialty websites and applications, to assessment, to monitoring outcomes. Although the educational technology sector continues to advance at a breakneck pace, an integrated learning data approach will be critical to unlocking institutional efficiency, directing appropriate change, and ensuring student success.
