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Ph.D.
(Computer Applications)
A RECOMMENDATION MODEL TO RESOLVE CAVITY TO
IMPROVE STUDENT OUTCOMES

Ph.D. Scholar : Trivedi Het Tusharbhai
Research Supervisor : Dr. Ajaykumar M. Patel

Regi. No.: 17276211012
Abstract :
In education, the most important par for any institute is their student’s performance and
their student’s outcomes. Every institutes works very hard to increase their student’s
performance. The organization always arrange extra sessions for students, they also
provides proper trainings for corporate world. All the faculties of the institute also make a
big role in improving student’s performance by providing proper guidance.

In this research, I have work on how institutes can able to identify various major factors
which directly or indirectly will effects student’s performance. For that, educational data
mining is the best way to identify proper results using different parameters and datasets.
In educational data mining there are various of algorithms available which can be very
much helpful to gain proper results from the datasets. There are various of algorithms are
available in Data mining, Classification rules, regression rule, Association rule mining are
most useful and will provides accurate and faster results from various datasets. In this
research, I have worked with association rule mining algorithms. In association rule
mining there are several of algorithms available like, apriori algorithm, FP Growth
algorithm, Eclat algorithm. This different algorithms will use the support and confidence
values of our datasets and on the basis of that it will find and provides the best rules
based on our parameters and datasets. In this research, I have used Apriori algorithm and
using that I have found best rules on the basis of my parameters and data sets. For my
study, I have taken a set of 20000 students data using the questioner from various
institutes and after finding those dataset I have applied it on Apriori algorithm and found
some rules. After that I have used that existing apriori algorithm and made several
changes and generates a new algorithm and apply same parameters and dataset on my
new algorithm it will provides me same rules with better confidence level.
Key words: Data mining, Association Rule Learning, Apriori algorithm, Data Collection,
WEKA, Decision Tree, Decision Table, Student’s Performance, Factors affecting.

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