In recent years, the field of education has seen a growing interest in the use of data mining techniques to improve teaching, learning, and administrative decision-making. Data mining refers to the process of uncovering hidden patterns, correlations, and useful information from large volumes of data. It involves applying various computational and statistical methods to analyze datasets that are often too complex or vast for traditional aanalysis methods. In the context of education, it can be used to analyze student performance, predict academic success, identify at-risk students, personalize learning paths, and improve curriculum design. By using classification algorithms, educational institutions can categorize student data and outcomes. This allows educators to develop targeted interventions and support systems that address individual student needs. Clustering techniques can group students by performance or engagement levels, offering insights into effective teaching strategies for different learner groups. Histograms, regression and correlative analysis can reveal broader trends, such as the impact of teaching methods or the effectiveness of online learning tools. These insights help institutions make data-driven decisions that enhance the overall educational experience. The present study analyzes a dataset of student registration numbers and their respective grades across all subjects. Alphabetic grades were converted to numeric values to facilitate analysis. Histograms of all subjects' grades were generated, followed by linear regression and a correlation matrix to identify relationships between subjects. The outputs include scatter plots for linear regression results and a correlation matrix for subject relationships.
| Published in | American Journal of Education and Information Technology (Volume 9, Issue 1) |
| DOI | 10.11648/j.ajeit.20250901.18 |
| Page(s) | 57-68 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Linear Regression, Machine Learning, Academic Data Analysis, Correlation Matrix, Student Grade Analysis
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APA Style
Atukuri, S. S., Busam, S., Khushalani, B. (2025). Exploring Academic Trends with Histograms, Linear Regression, and Correlation Matrix. American Journal of Education and Information Technology, 9(1), 57-68. https://doi.org/10.11648/j.ajeit.20250901.18
ACS Style
Atukuri, S. S.; Busam, S.; Khushalani, B. Exploring Academic Trends with Histograms, Linear Regression, and Correlation Matrix. Am. J. Educ. Inf. Technol. 2025, 9(1), 57-68. doi: 10.11648/j.ajeit.20250901.18
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Download@article{10.11648/j.ajeit.20250901.18,
author = {Satya Sri Atukuri and Sruthi Busam and Bharat Khushalani},
title = {Exploring Academic Trends with Histograms, Linear Regression, and Correlation Matrix
},
journal = {American Journal of Education and Information Technology},
volume = {9},
number = {1},
pages = {57-68},
doi = {10.11648/j.ajeit.20250901.18},
url = {https://doi.org/10.11648/j.ajeit.20250901.18},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajeit.20250901.18},
abstract = {In recent years, the field of education has seen a growing interest in the use of data mining techniques to improve teaching, learning, and administrative decision-making. Data mining refers to the process of uncovering hidden patterns, correlations, and useful information from large volumes of data. It involves applying various computational and statistical methods to analyze datasets that are often too complex or vast for traditional aanalysis methods. In the context of education, it can be used to analyze student performance, predict academic success, identify at-risk students, personalize learning paths, and improve curriculum design. By using classification algorithms, educational institutions can categorize student data and outcomes. This allows educators to develop targeted interventions and support systems that address individual student needs. Clustering techniques can group students by performance or engagement levels, offering insights into effective teaching strategies for different learner groups. Histograms, regression and correlative analysis can reveal broader trends, such as the impact of teaching methods or the effectiveness of online learning tools. These insights help institutions make data-driven decisions that enhance the overall educational experience. The present study analyzes a dataset of student registration numbers and their respective grades across all subjects. Alphabetic grades were converted to numeric values to facilitate analysis. Histograms of all subjects' grades were generated, followed by linear regression and a correlation matrix to identify relationships between subjects. The outputs include scatter plots for linear regression results and a correlation matrix for subject relationships.
},
year = {2025}
}
TY - JOUR T1 - Exploring Academic Trends with Histograms, Linear Regression, and Correlation Matrix AU - Satya Sri Atukuri AU - Sruthi Busam AU - Bharat Khushalani Y1 - 2025/06/16 PY - 2025 N1 - https://doi.org/10.11648/j.ajeit.20250901.18 DO - 10.11648/j.ajeit.20250901.18 T2 - American Journal of Education and Information Technology JF - American Journal of Education and Information Technology JO - American Journal of Education and Information Technology SP - 57 EP - 68 PB - Science Publishing Group SN - 2994-712X UR - https://doi.org/10.11648/j.ajeit.20250901.18 AB - In recent years, the field of education has seen a growing interest in the use of data mining techniques to improve teaching, learning, and administrative decision-making. Data mining refers to the process of uncovering hidden patterns, correlations, and useful information from large volumes of data. It involves applying various computational and statistical methods to analyze datasets that are often too complex or vast for traditional aanalysis methods. In the context of education, it can be used to analyze student performance, predict academic success, identify at-risk students, personalize learning paths, and improve curriculum design. By using classification algorithms, educational institutions can categorize student data and outcomes. This allows educators to develop targeted interventions and support systems that address individual student needs. Clustering techniques can group students by performance or engagement levels, offering insights into effective teaching strategies for different learner groups. Histograms, regression and correlative analysis can reveal broader trends, such as the impact of teaching methods or the effectiveness of online learning tools. These insights help institutions make data-driven decisions that enhance the overall educational experience. The present study analyzes a dataset of student registration numbers and their respective grades across all subjects. Alphabetic grades were converted to numeric values to facilitate analysis. Histograms of all subjects' grades were generated, followed by linear regression and a correlation matrix to identify relationships between subjects. The outputs include scatter plots for linear regression results and a correlation matrix for subject relationships. VL - 9 IS - 1 ER -
Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, Bhimavaram, India
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