Educational Data Mining involves applying data mining and statistical techniques to information from educational institutions to help analyze student performance. It identifies patterns in large datasets that can help predict student choices, assess their knowledge over time, and help administrators and teachers improve the educational experience. While useful, Educational Data Mining also raises privacy and security issues regarding student data.

1. Educational Data Mining
in predicting Students’
Performances
by
Nisha (ID 45121)&
Priyanka (ID 45065)

2. Data Mining: It is also known as Knowledge Discovery in Data (KDD). It is a process that uses different
data analysis tools to explore patterns or rules from huge amount of data. Also it helps in finding the
relationships in data which can be used to make valid and accurate predictions in different areas.
Data Mining is multi-disciplinary
field which combines AI statistics
and database technology. Ref
[Puppala Priyanka].
Fig: process of data mining [Ref:
https://www.rfwireless-
world.com/Tutorials/Data-Mining-
tutorial.html ]
•

3. What is EDM (Educational Data Mining)
• It is an application of Data Mining and statistics to
information generated from colleges and universities.
[Ref: Informational Educational Data Mining
Society,2011] it was introduced to tackle the
problems of traditional data mining algorithms. To
organise huge data of whole institution, a pre-
processor algorithm was introduced, popularly known
as Clustering.
Benefits of EDM
• Identifying student choices and interest of study.
(specialisation)
• Predicting knowledge of students by evaluating their
grades and prepare results.
• Helping management to manage all financial and
academic activities.
• Automatic exploration of data
• Saving valuable time of students as well as authorities.
• Helps in establishing good relationship between
students and teachers.

4. Achievements:
• Active and collaborative participation of students in the class.
• Students are updated with the changes in curriculum, financial updates, latest events
to prepare themselves.
• Teachers are getting direct feedback from student so that they can improve more
teaching style.
• By analysing student’s interest for one subject in particular, professors can guide him
timely for successful career.
• Can get access to all valuable sources of study.
Future Work:
• We will look into other techniques of data mining (multimedia data mining,
distributed data mining, sequence data mining and such alike) in educational field
which are used to predict student’s academic performance.
• Where actually data mining is used and for what purposes.
• We will discuss about the security and privacy issues involved in data mining.

5. Conclusion:
• Data mining in Education sector helps the university/college to detect the student’s
involvement areas and to determine their performances.
• DM in education can help both the student and the management as well.
• DM does have some privacy and security issues.
• It will also help the student to improve in their academic performance.

6. Reference 2nd Diagram
[https://www.win.tue.nl/~mpechen/projects/e
dm/]