PREDICTING STUDENT DROPOUT USING MACHINE LEARNING METHODS
Mai Thi My Tien , Tran Thanh Nam , Nguyen Van Linh , Ngo Ho Anh Khoi
Abstract: Currently, the phenomenon of student dropout midway through their education is a concerning issue. This not only directly impacts the lives of students and their families, but also affects higher education institutions and society as a whole. Consequently, this situation leads to various difficulties for students, coupled with a lack of soft skills and life experience, causing many students to turn to part-time jobs. This research aims to construct a machine learning model to predict students' academic outcomes and dropout status. This contributes to identifying causes and effectively addressing the mentioned issues, thereby alleviating burdens on families and society, limiting social problems, boosting economic growth, creating more job opportunities, and enhancing competitiveness and productivity. This dataset is highly valuable for researchers seeking to conduct comparative studies on students' academic achievements and for training in the field of machine learning.
Keywords: Predict Student Dropout Dataset, Predicting Student Dropout using Machine Learning Methods, GaussianNB Classifier, application
Student Dropout Attributes
| # | Name | Type | Describe |
|---|---|---|---|
| 1 | Marital status | Float | 1 - Single, 2 - Marry, 3 - Divorce |
| 2 | Application mode | Float | Various descriptions provided |
| 3 | Application order | Float | From 1 to 9 (default is 1) |
| 4 | Course | Float | Various course options |
| 5 | Daytime evening attendance | Float | 1 - Daytime, 0 - Evening |
| 6 | Previous qualification | Float | Various qualification options |
| 7 | Nationality | Float | 1 - Portugal, 2 - Other |
| 8 | Mother’s qualification | Float | Various qualification options |
| 9 | Father’s qualification | Float | Various qualification options |
| 10 | Mother’s occupation | Float | Various occupation options |
| 11 | Father’s occupation | Float | Various occupation options |
| 12 | Displaced | Float | 1 - Yes, 0 - No |
| 13 | Educational special needs | Float | 1 - Yes, 0 - No |
| 14 | Debtor | Float | 1 - Yes, 0 - No |
| 15 | Tuition fees up to date | Float | 1 - Yes, 0 - No |
| 16 | Gender | Float | 1 - Male, 0 - Female |
| 17 | Scholarship holder | Float | 1 - Yes, 0 - No |
| 18 | Age at enrollment | Float | From 17 to 70 |
| 19 | International | Float | 1 - Yes, 0 - No |
| 20 | Curricular units 1st sem (credited) | Float | From 0 to 20 |
| 21 | Curricular units 1st sem (enrolled) | Float | From 0 to 26 |
| 22 | Curricular units 1st sem (evaluations) | Float | From 0 to 45 |
| 23 | Curricular units 1st sem (approved) | Float | From 0 to 26 |
| 24 | Curricular units 1st sem (grade) | Float | From 0.000 to 18.875 |
| 25 | Curricular units 1st sem (without evaluations) | Float | From 0 to 12 |
| 26 | Curricular units 2nd sem (credited) | Float | From 0 to 19 |
| 27 | Curricular units 2nd sem (enrolled) | Float | From 0 to 23 |
| 28 | Curricular units 2nd sem (evaluations) | Float | From 0 to 33 |
| 29 | Curricular units 2nd sem (approved) | Float | From 0 to 20 |
| 30 | Curricular units 2nd sem (grade) | Float | From 0.000 to 18.571 |
| 31 | Curricular units 2nd sem (without evaluations) | Float | From 0 to 12 |
| 32 | Unemployment rate | Float | From 7.600 to 16.200 |
| 33 | Inflation rate | Float | From -0.800 to 3.700 |
| 34 | GDP | Float | From -4.100 to 3.500 |
CLICK TO KNOW MORE
