Applications of Data Science in Education

1. APPLICATIONS OF
DATA SCIENCE IN
EDUCATION
A N A M I K A J A Y A R A J
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2 M A E C O

2. CONTENTS
EDUCATION AS AN INDUSTRY
CHALLENGES IN THE FIELD
HOW DATA SCIENCE CAN HELP THE INDUSTRY
APPLICATIONS
CONCLUSION

3. EDUCATION AS AN INDUSTRY
The Education Industry comprises of establishments whose primary
objective is to provide education.
These establishments can be public, non-profit, or for-profit institutions.
They include elementary schools, secondary schools, community
colleges, universities, and ministries or departments of education.
Educational institutions contribute, in a major way, in the development
of this resource. They are tasked with providing the talent pool for other
industries, and this is critical when competing in a global economy.
Governments are expected to develop and implement strategies that
promote education and enrollment of students in higher education.
High-enrollment in higher education can translate into more skilled
labor, which can provide a positive impact on a country’s GDP.

4. CHALLENGES FACED BY
THE EDUCATION INDUSTRY
Inaccessible environments not catering
to student diveristy
Rigid curriculums and pedagogy
Lack of proper teaching aides
Lack of real world applications to what's
being taught
Outdated use of technology for
coursework

5. HOW DATA SCIENCE CAN HELP
BETTER EDUCATION
Educational institutions and learning process entail rich data, making it a
well-suited domain for data science. Data science of classroom interactions
can capture how classroom management and instruction is accomplished.
As video and voice recordings grow more prevalent, it may be a prime data
source to analyze. Educational data extends to the higher education realm
where online courses are being employed.
It even extends to the private sector where online forums, threads, and
distributed forms of problem-solving are used to educate employees and
resolve task problems.
All these new data sources are replete with information on communication
(text), relations(links), and accruing behavioral profiles (careers) and can be
mined and analyzed in an effort to understand and solve persistent
educational problems.

6. APPLICATIONS IN EDUCATION
1) Curriculum data:
With the amount of competition increasing in the field of education, schools and
universities need to stay up to date with industry expectations in order to deliver
relevant and improved courses to their students. Data science may be beneficial for
studying industrial patterns and assisting course makers in imbibing relevant
subjects by using various statistical measurements and monitoring approaches.
Furthermore, colleges may use predictive analytics to evaluate demand for new
skill sets and tailor courses to meet those needs.
2) Student Assessment Data:
In a classroom, there are many different types of students who are taught at once
by a single teacher. It is very common for a percentage of students to excel and for
a number of students to not understand the class properly. Assessment data can
help the teachers determine their students’ understanding and modify their
teaching strategies for the future.

7. 3) Anticipate Student Graduation and Dropout Rates
Higher education institutions are increasingly leveraging data science in
education and machine learning solutions to predict scenarios such as which
students are most likely to enroll, graduate, and be ready for a career in their
chosen area of study. These capabilities also help educational providers track
patterns in student dropout rates and the corresponding demographic and
educational factors to predict potential future dropouts so they can
proactively intervene and allocate resources to prevent it.
4) Deeper Understanding of Student Progress
Advanced analytics, including AI, is also being used to gain insights into
academic performance so that teachers, faculty, parents, and students can
better understand how a student is responding to certain tests, for example.
This information can then be applied to modify the corresponding learning or
teaching path to improve academic outcomes.

8. CONCLUSION
Data Science has great potential to change how education is
facilitated in the future. Both students and teachers will find
themselves with a lot of flexibility on how to go about a course
with the use of data science in education.
Furthermore, this will be a game changer as accessibility will
widen to pool of students without being bound to traditional
teaching methods as coursework can be adapted to their
special needs without letting them fall behind their
classmates.
In conclusion, applying data science to the education industry
will make the industry very efficient and help produce highly
skilled graduates.

9. THANK YOU