This document discusses factors that contribute to programming skill and grade point average (GPA) for computer science graduates. It aims to predict student performance in these areas by analyzing personal interests, academic results, analytical skills, and problem solving skills. The author developed machine learning models to extract the most significant predictive features for prospective computer science students. This would help students decide if computer science is the right program for them and potentially help them improve in areas that influence success in the field.

1. Factors that contribute programming skill and
CGPA as a CS graduate: Mining Educational Data
Md.Jahedul Karim
Department of CSE
International Islamic University Chittagong(IIUC)
Chittagong, Bangladesh
jahedulkarimpappu20@gmail.com
Abstract—Computer Science (CS) has become one of the most
popular under graduate program in last few years. According to
UGC roughly 116 universities out of 136 are offering computer
science program which indicates a massive number of students
are choosing this program as their undergraduate program. But
statistically significant number of students are failing to become
skilled and effective CS graduate because many students are
taking CS without accessing their chance in this program. Success
in academic and professional life require to choose right under
graduate program. Considering CGPA and Programming Skill as
two of the most significant factors to determine students success
in CS, we have predicted these two by taking students personal
interest, academic results, analytical skill and problem solving
skill into account. We also extracted most significant features of
a prospective CS student by using gain ratio.
Index Terms—Computer Science Student, Predicting Perfor-
mance, Machine Learning Techniques, Data Mining, Program-
ming skill, CGPA
I. SUMMARY
Computer Science has now become a buzzword in the global
community. Being one of the developing countries Bangladesh
Government has already taken the challenge of outshining
in the ICT department, so as the students. But when the
question of skill, show casing talent and achievements in
national and international level comes it seems significantly
important percent of students are failing to do so. Without
proper analysis, substantial amount of students are taking this
program and eventually the performance of larger part of
students of this Program is performing poorly which is hurting
their Academic and professional life. Though massive number
of students are rushing into this program in Bangladesh,
many IT industries are still hiring IT professionals from India
because of limited number of skilled graduates [1]. So, to
predicting the performance of prospective CS graduates before
they start is what they need. If they can know the factors on
what their performance as a CS graduate depends, they can
decide whether they are going to take CS or not. There is a
possibility that they can change themselves as the demands to
be a good CS graduate.
II. CONTRIBUTION
Improving the performance of a database system is one of
the keyresearch issues now a day. Distributed processing is
an effectiveway to improve reliability and performance of a
database system.Distribution of data is a collection of fragmen-
tation, allocationand replication processes. Previous research
works providedfragmentation solution based on empirical data
about the type andfrequency of the queries submitted to a
centralized system. Thesesolutions are not suitable at the
initial stage of a database designfor a distributed system.
In this paper we have presented afragmentation technique
that can be applied at the initial stage aswell as in later
stages of a distributed database system forpartitioning the
relations. Allocation of fragments is donesimultaneously in our
algorithm.
III. LIMITATION
Before reasonable amount of statistical record are available
for constructing attribute affinity matrix or predicate affinity
matrix and to fragment and allocate the database among the
three sites, percentage of hit of the overall system is only 33.33
percent which is much less in comparison with our achieved
85 percent hit rate. The reason of poor performance of TWIF
is that, all sites other than central site have no data.
IV. CONCLUSION
Making proper fragmentation of the relations and allocation
of the fragments is a major research area in DDBMS. In
this paper we have presented a fragmentation technique to
partition relations of a distributed database properly at the
initial stage. We have also addresses some important scalability
issues and provides some algorithms to ensure generality of
our developed MMF technique. So performance of a DDBMS
can be improved significantly by avoiding frequent remote
access and high data transfer among the sites. This research
can be extended to support fragmentation in distributed object
oriented databases as well.
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