Using Ensemble Decision Tree Model to Predict Student Dropout in Computing Science

Mohammed, Naseem and Kaylash, Chaudhary and Bibhya, Sharma and Goel, Lal (2020) Using Ensemble Decision Tree Model to Predict Student Dropout in Computing Science. [Conference Proceedings]

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Abstract

Science, Technology, Engineering and
Mathematics (STEM) professionals play a key role in the
development of an economy. STEM workers are critical
thinkers as they contribute immensely by driving innovations.
There is a high demand for professionals in the STEM fields
but there is also a shortage of human resource in these areas.
One way to reduce this problem is by identifying students who
are at-risk of dropping out and then intervening with focused
strategies that will ensure that these students remain in same
the programme till graduation. Therefore, this research aims
to use a data mining classification technique to identify
students who are at-risk of dropping out from their Computing
Science (CS) degree programmes. The Random Forest (RF)
decision tree algorithm is used to learn patterns from historical
data about first-year undergraduate CS students who are
enrolled in a tertiary institute in the South Pacific. A number
of factors are used which comprise of students demographic
information, previous education background, financial
information as well as data about students’ academic
interaction. Feature selection is performed to determine which
factors have greater influence in students’ decision in dropping
out. Cross-validation techniques are used to ensure that the
models are not over-fitted. Two models were built using a 5-
fold and 10-fold cross-validation and the results were
compared using several measures of model performance. The
results show that the factors corresponding to students’
academic performance in a first-year programming course had
the greatest impact student attrition in CS

Item Type:	Conference Proceedings
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User:	Goel Lal
Date Deposited:	07 Aug 2025 00:37
Last Modified:	07 Aug 2025 00:37
URI:	https://repository.usp.ac.fj/id/eprint/15074

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