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Academic Performance Forecasting Data
Education & Learning Analytics
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Free
About
This dataset contains the semester results, specifically Cumulative Grade Point Averages (CGPA), of technical students. Its primary purpose is to enable the prediction of a student's next semester CGPA by analysing their past academic performance. The data was meticulously collected manually from the official website of a Technical University. While student roll numbers have been anonymised for privacy, college codes, subject details, and the semester CGPA values are authentic real-world data points. This dataset is ideally suited for developing machine learning models, particularly those employing advanced regression techniques, to forecast academic outcomes.
Columns
- 1st cgpa: Cumulative Grade Point Average for the first semester, ranging from 3.85 to 9.15.
- 2nd cgpa: Cumulative Grade Point Average for the second semester, ranging from 3.90 to 9.21.
- 3rd cgpa: Cumulative Grade Point Average for the third semester, ranging from 3.96 to 9.59.
- 4th cgpa: Cumulative Grade Point Average for the fourth semester, ranging from 4.29 to 9.31.
- 5th cgpa: Cumulative Grade Point Average for the fifth semester, ranging from 4.00 to 9.46.
- College Code: A numerical identifier for the college.
- Gender: The gender of the student, categorised as 'Male', 'M', or 'Other'.
- Roll: An anonymised student roll number, useful for unique student identification.
- Roll no.: An alternative representation of the anonymised student roll number, presenting a different subset of data.
- Subject Code: A numerical identifier for the academic subject.
Distribution
The dataset is provided as a
data.csv file, approximately 8.06 KB in size, and includes 10 distinct columns. The number of records varies per column due to differing levels of data availability, with up to 178 valid entries for certain attributes.Usage
This dataset is well-suited for a variety of applications, including:
- Predicting student academic performance: Specifically, forecasting the next semester's CGPA.
- Analysing academic trends: Gaining insights into student progression and performance patterns.
- Developing machine learning models: Ideal for training and evaluating regression algorithms aimed at educational forecasting.
- Statistical analysis: Conducting in-depth statistical studies on factors influencing student results.
Coverage
The dataset focuses on technical students, drawing information from a Technical University. It encompasses academic performance metrics across five semesters. Demographic information, including gender, is included. The specific geographic scope is limited to the context of the collecting university, and the time range is implied by the sequential semester data.
License
CC0: Public Domain
Who Can Use It
- Academic Researchers: To explore educational methodologies, student success, and attrition.
- Data Scientists: For building predictive analytics solutions in the education sector.
- Educational Institutions: To monitor student progress, identify at-risk students, and inform academic support strategies.
- Developers: To create tools and applications that leverage student performance data for educational planning.
Dataset Name Suggestions
- Student CGPA Prediction Dataset
- Technical University Student Results
- Academic Performance Forecasting Data
- Student Semester Grades
- University Student Progression
Attributes
Original Data Source: Academic Performance Forecasting Data
QUALITY