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Student Grading Prediction Analytic
Education & Learning Analytics
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About
This dataset is designed to facilitate the prediction of students' end-of-term academic performances using machine learning techniques. It encompasses a range of factors including personal details, family background, and study habits, providing a robust foundation for developing predictive models that can identify influential variables impacting student success.
Columns
The dataset contains 33 columns, each offering unique insights into student characteristics and behaviours:
- STUDENT ID: A unique identifier for each student.
- Student Age: Categorised age groups (1: 18-21, 2: 22-25, 3: above 26).
- Sex: Gender of the student (1: female, 2: male).
- Graduated high-school type: Type of high school attended (1: private, 2: state, 3: other).
- Scholarship type: The level of scholarship received (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full).
- Additional work: Indicates if the student has additional work commitments (1: Yes, 2: No).
- Regular artistic or sports activity: Indicates participation in regular artistic or sports activities (1: Yes, 2: No).
- Do you have a partner: Indicates if the student has a partner (1: Yes, 2: No).
- Total salary if available: Categorised salary ranges (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410).
- Transportation to the university: Mode of transport used to get to university (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other).
- Accommodation type in Cyprus: Type of accommodation in Cyprus (1: rental, 2: dormitory, 3: with family, 4: Other).
- Mothers’ education: Level of mother's education (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.).
- Fathers’ education: Level of father's education (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.).
- Number of sisters/brothers (if available): Number of siblings (1: 1, 2: 2, 3: 3, 4: 4, 5: 5 or above).
- Parental status: Parental relationship status (1: married, 2: divorced, 3: died - one of them or both).
- Mothers’ occupation: Mother's occupation (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other).
- Fathers’ occupation: Father's occupation (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other).
- Weekly study hours: Categorised weekly study hours (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours).
- Reading frequency (non-scientific books/journals): Frequency of reading non-scientific materials (1: None, 2: Sometimes, 3: Often).
- Reading frequency (scientific books/journals): Frequency of reading scientific materials (1: None, 2: Sometimes, 3: Often).
- Attendance to the seminars/conferences related to the department: Indicates attendance at departmental seminars/conferences (1: Yes, 2: No).
- Impact of your projects/activities on your success: Perceived impact of projects/activities on success (1: positive, 2: negative, 3: neutral).
- Attendance to classes: Class attendance regularity (1: always, 2: sometimes, 3: never).
- Preparation to midterm exams 1: Study method for the first midterm exam (1: alone, 2: with friends, 3: not applicable).
- Preparation to midterm exams 2: Study timing for the second midterm exam (1: closest date to the exam, 2: regularly during the semester, 3: never).
- Taking notes in classes: Note-taking habits in classes (1: never, 2: sometimes, 3: always).
- Listening in classes: Listening habits in classes (1: never, 2: sometimes, 3: always).
- Discussion improves my interest and success in the course: Perceived impact of discussion on interest and success (1: never, 2: sometimes, 3: always).
- Flip-classroom: Perceived usefulness of flip-classroom teaching (1: not useful, 2: useful, 3: not applicable).
- Cumulative grade point average in the last semester (/4.00): Categorised GPA from the last semester (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49).
- Expected Cumulative grade point average in the graduation (/4.00): Categorised expected GPA at graduation (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49).
- Course ID: Identifier for the course.
- OUTPUT Grade: The final grade achieved (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA).
Distribution
This dataset is provided in CSV format, specifically as
StudentsPerformance_with_headers.csv. It has a file size of 11.65 kB and contains 33 columns. The dataset comprises 145 individual student records. It is a static dataset with no expected future updates.Usage
This dataset's primary purpose is to enable the development and application of machine learning techniques for predicting students' end-of-term academic performance. It is ideal for building models to forecast grades and identify key factors influencing educational outcomes.
Coverage
The dataset focuses on students, with demographic information including age, sex, high school background, and various socio-economic factors. The inclusion of "Accommodation type in Cyprus" suggests a geographic scope primarily centred on students studying in Cyprus. There is no specific time range mentioned for data collection, but the dataset is noted as having no expected updates.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is well-suited for researchers, data scientists, and educators. It can be utilised for a variety of purposes, including:
- Developing and testing machine learning models for academic performance prediction.
- Conducting statistical analyses to understand correlations between student attributes and grades.
- Informing educational strategies and interventions aimed at improving student outcomes.
- Identifying students at risk of underperformance to provide targeted support.
Dataset Name Suggestions
- Student Academic Performance Predictor
- University Student Outcome Data
- Student Success Factors Dataset
- Academic Performance ML Dataset
- Student Grading Prediction Analytics
Attributes
Original Data Source: Student Grading Prediction Analytic
QUALITY