Speaker
Description
Student performance analysis is an important area in educational data science. This project focuses on building a Student Performance Analyzer to study and evaluate academic performance using collected data such as marks, attendance, and study hours. The objective is to use statistical analysis and data visualization techniques to understand patterns in student performance and support better academic decision-making.
The system collects and processes student data including subject-wise marks, attendance percentage, and study time. Using these variables, the project analyzes how different factors influence academic results. Statistical models and exploratory data analysis are applied to identify trends such as the relationship between attendance and marks, variation in subject performance, and indicators of strong or weak students.
The project also applies basic predictive modeling techniques to estimate final academic outcomes based on available inputs. By analyzing patterns in historical data, the system can help predict whether a student is likely to perform well or require additional academic support.
Visualization plays an important role in this analysis. Graphs, charts, and dashboards are used to present insights clearly for teachers, administrators, and students. These visualizations allow easy comparison of subject performance, distribution of marks, and correlations between different variables.
The insights generated by the analyzer can help educational institutions identify students who need assistance, understand factors affecting academic outcomes, and improve learning strategies. In the long term, such systems can contribute to more data-driven approaches in education.
This project demonstrates how data analysis, statistical modeling, and visualization techniques can be applied in the education domain to transform raw academic data into meaningful insights that support better educational planning and student development.
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ChatGPT was used only to help improve the wording and clarity of the submission text.
Additional Material or Paper
No prior publication. This work is part of an academic project.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | student performance analysis, educational data mining, data visualization, predictive modeling, statistical analysis |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |