This presentation summarizes a research paper that used machine learning to predict the performance and dropout rates of computer science students in Bangladesh. The research collected data from current computer science students and used algorithms like SVM, naive Bayes, and neural networks. The models could predict student GPA, programming skills, and likelihood of dropping out with up to 98.2% accuracy. The research identified key factors like prior academic results that influence student success. The findings could help students and universities by identifying those at risk of dropping out and supporting students to achieve better results.

1. Presentation for Software Engineering
Presented by
• Taminul Islam - 181-15-11116
• Rishalatun Jannat Lima - 181-15-11120
• Arindom Kundu - 181-15-10557
• Md Al-Amin Hosen - 181-15-11132
Presented to
Mr. Abdus Sattar
Assistant Professor
Department of Computer Science and Engineering
Daffodil International University
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2. A Machine Learning Approach to Performance and Dropout
prediction in Computer Science: Bangladesh Perspective
Title
Sheikh Arif Ahmed Md. Aref Billah Shahidul Islam Khan
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Reference: Ahmed, S. A., Billah, M. A., & Khan, S. I. (2020, July). A Machine Learning Approach to Performance and Dropout prediction in Computer Science: Bangladesh Perspective. In 2020 11th
International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.

3. Abstract
Nowadays Computer Science (C.S.) and other technology-related subjects are a hot cake for the students. Due to a
good job market for these subjects, students are taking computer science and other related topics without thinking
about their capability and without knowing the curriculum of these subjects. So the dropout rate is getting high day
by day in these subjects. Especially developing countries like Bangladesh. In this work, they have used current
computer science students’ data to predict their and also prospective C.S. students’ future performance and the
chance of dropout using machine learning algorithms.
They have used SVM, naïve Bayes, neural network, etc. They have also predicted the crucial factors that are strongly
correlated to the performance of a C.S. student.
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4. Introduction
 Objectives
 To track down the original reasons behind the Dropout of Computer Science graduates.
 To predict and prospective Computer Science students’ future performance
 To predict the chance of dropout
 To predicted the crucial factors which are strongly correlated to the performance of a Computer Science student.
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5. Introduction
 Research Goal
Taking Computer Science students live and current data from all over the world then create an algorithm to predict
the future performance and the chance of dropout using machine learning algorithms like SVM, naïve Bayes, neural
network.
Also predict the crucial factors that are strongly correlated to the performance of a C.S. student. Finding reasons
behind the dropout on Computer Science students.
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6. Introduction
 Research Question
1. What are the actual reason behind the dropout on Computer Science students ?
2. How a dropout student contributes in the global world nationally & internationally ?
3. What are the main factors behind the success and failure of a dropout student on C.S department ?
4. What are the most efficient indicators to analysis a student ?
5. How to make development of a C.S student to avoiding dropout ?
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7. Literature Review
 Dropout Prediction
• Ahmed and Khan discovered few essential features like the previous result, math score of school-level, etc. responsible for
student dropout while predicting the perspective dropout student using machine learning.
He also showed, how CGPA and programming skill impacts future dropouts.
• Vinayak and Prageeth predicted students dropout using 54 attributes, which includes personal and health information as well as
the previous academic data.
• Costa et al. used only a course data to predict perspective dropout students. While Boris et al. didn’t take any survey from
students. They took data from institutions to make their dropout prediction model using machine learning algorithms
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8. Literature Review
 Performance Prediction
• Alharbi et al. collected students’ data to predict the performance after completing one year in the university
• Baradwaj & Pal predicted students’ performance using a database collected from the university, which includes their personal
and academic data they filled up during admission.
• Goga et al. proposed a tool using classification algorithms to predict students’ performance. They used multi-layer perceptions,
random forests, etc. to build the model.
• Arsad & Buniyamin found that whoever has a good foundation for the previous study has an excellent performance.
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9. Research Methods
 Data Collection and Preprocessing
Using the questionnaire, data were
collected through IBM SPSS and
Google Form.
Figure 1- Attributes Overview
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10. Research Methods
 Data Collection and Preprocessing
Figure 2 shows the options with
correspondent values, and short
term of the survey questionnaire
Figure 2- Short Terms
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11. Research Methods
 Data Collection and Preprocessing
Table 1:
Students related variables that illustrate
the questions they asked the students and
probable answers.
Table 1
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12. Research Methods
 Data Collection and Preprocessing
They have collected data from the current C.S. student of various universities
from the diverse city of Bangladesh. Table II shows the frequency of gender
from the dataset.
Table III shows the sample rule generation for the new feature
creation.
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13. Research Methods
 Data Mining
In this section, they have showed a
successful way to make prediction
model by classification via clustering
method which we have followed as a
structure.
i) Predicted Programming Skill and
CGPA
ii) Predicted the chances of dropout
Figure 3. Model workflow Overview
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14. Main Work
Predicting Programming Skill and CGPA
The decision tree , SVM, Neural network and Random Forest was used for building the model. While
predicting the CGPA, they had to group the CGPA on a scale of 1 to 5, which shown in table 8.
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15. Main Work
Then they have predicted the CGPA and Programming Skill using different algorithms. Figure 4 shows the model
building process-
Figure 4. Prediction model for Predicting CGPA and Programming Skill.
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16. Main Work
CGPA Prediction
For an illustration of results, we used R.O.C. curves with F.P. rate (Specificity) in the X-axis and T.P. rate
(Sensitivity) in the Y-Axis ( figure 5,7 and 9)
Results show “Random Forest” algorithms perform better than others. For further evaluation, the R.O.C. curve is
shown in Figure 5-
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17. Main Work
Programing Skill Prediction
Figure 6. Results of Programming Skill Prediction
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18. Main Work
Dropout Prediction
Figure 8. Results for Predicting Dropout
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19. Result & Conclusion
Unlike other dropout prediction works, they have taken a few attributes related to C.S. Again proved that
CGPA and Programming skill is very crucial for predicting perspective dropout.
This model can predict and notify a student before starting the undergraduate program, whether they are fit
for a C.S. undergraduate. Also, students can know how their CGPA and programming skill will be using
their current data.
the results of the dropout prediction model say all — best Accuracy by the Neural network, which is
98.2%.
This work can be beneficial to the students, whoever thinking of starting an undergraduate in CS-related
subjects but not limited to them.
Also, the students in the midway can take help from the predicted feature set for a good result and
programming skill to have a bright career.
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20. Motivation from this study
Absolutely this is an outstanding achievement for Bangladeshi researcher. This study provides 98.2%
accuracy, which is marvelous.
1) They have discovered the ten most influential features to get success in C.S.E. Predicting CGPA,
Programing skills and dropout classifier is most important for a student. This is a great contribution on
education sector.
2) They have also predicted the final result & performance.
3) Students will be benefitted more by implementing this study.
4) This will help to produce the productive person in the job market specially in CSE background.
5) Discovering essential factor for an excellence performance is one of the biggest achievement from this
study.
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21. FAQ
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22. Thank You
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