Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression Analysis
Understanding student academic performance is a cornerstone for developing sustainable educational practices that benefit students, teachers, policymakers, and society. This analysis directly impacts students’ ability to engage in and promote sustainable practices, thereby shaping their f...
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2025-01-01
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| author | Mohamed El Jihaoui Oum El Kheir Abra Khalifa Mansouri |
| author_facet | Mohamed El Jihaoui Oum El Kheir Abra Khalifa Mansouri |
| author_sort | Mohamed El Jihaoui |
| collection | DOAJ |
| description | Understanding student academic performance is a cornerstone for developing sustainable educational practices that benefit students, teachers, policymakers, and society. This analysis directly impacts students’ ability to engage in and promote sustainable practices, thereby shaping their future academic success. While many studies focus on predicting student performance based on a set of features, our study takes an approach by reducing these features into factors and analyzing their impact. We aim to identify the factors influencing student performance within the middle school education system using a combined approach of Factor Analysis for Mixed Data (FAMD) and Multiple Linear Regression (MLR). Our analysis is based on a robust and reliable large dataset of 1,073,450 observations, encompassing qualitative and quantitative features. FAMD analysis identified four underlying factors: prior academic performance, academic delay, socioeconomic status, and class environment; all these factors have good to excellent reliability, with Cronbach’s Alpha values ranging from 0.809 to 0.930. Feeding these factors into the MLR produces a robust model that explains 88.53% of the variance in the CGPA, indicating a strong fit. Prior Academic Performance factor emerges as the most powerful predictor, accounting for 76.6% of the explained variance. Academic Delay follows, explaining 14.34% of the variance. Socioeconomic Status contributes 6.02%, and Class Environment adds 3.03%, reflecting smaller but meaningful impacts. All predictors are statistically significant (p <0.001), confirming their critical roles in influencing student performance (CGPA). The insights gained from this study are critically important in the field of education. They enable teachers and educational leaders to identify at-risk students early and develop targeted interventions that address the factors influencing their performance. This approach aims to enhance learning outcomes, improve educational practices, and promote sustainable education. |
| format | Article |
| id | doaj-art-8c95e52e22b94d47b118fcca90ed132e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-8c95e52e22b94d47b118fcca90ed132e2025-01-29T00:01:08ZengIEEEIEEE Access2169-35362025-01-0113159461596410.1109/ACCESS.2025.353209910847863Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression AnalysisMohamed El Jihaoui0https://orcid.org/0009-0009-6637-659XOum El Kheir Abra1https://orcid.org/0000-0001-6958-6570Khalifa Mansouri2Modeling and Simulation of Intelligent Industrial Systems Laboratory (M2S2I), ENSET Mohammedia, University Hassan II of Casablanca, Casablanca, MoroccoRegional Center for Education and Training Professions, Rabat, MoroccoModeling and Simulation of Intelligent Industrial Systems Laboratory (M2S2I), ENSET Mohammedia, University Hassan II of Casablanca, Casablanca, MoroccoUnderstanding student academic performance is a cornerstone for developing sustainable educational practices that benefit students, teachers, policymakers, and society. This analysis directly impacts students’ ability to engage in and promote sustainable practices, thereby shaping their future academic success. While many studies focus on predicting student performance based on a set of features, our study takes an approach by reducing these features into factors and analyzing their impact. We aim to identify the factors influencing student performance within the middle school education system using a combined approach of Factor Analysis for Mixed Data (FAMD) and Multiple Linear Regression (MLR). Our analysis is based on a robust and reliable large dataset of 1,073,450 observations, encompassing qualitative and quantitative features. FAMD analysis identified four underlying factors: prior academic performance, academic delay, socioeconomic status, and class environment; all these factors have good to excellent reliability, with Cronbach’s Alpha values ranging from 0.809 to 0.930. Feeding these factors into the MLR produces a robust model that explains 88.53% of the variance in the CGPA, indicating a strong fit. Prior Academic Performance factor emerges as the most powerful predictor, accounting for 76.6% of the explained variance. Academic Delay follows, explaining 14.34% of the variance. Socioeconomic Status contributes 6.02%, and Class Environment adds 3.03%, reflecting smaller but meaningful impacts. All predictors are statistically significant (p <0.001), confirming their critical roles in influencing student performance (CGPA). The insights gained from this study are critically important in the field of education. They enable teachers and educational leaders to identify at-risk students early and develop targeted interventions that address the factors influencing their performance. This approach aims to enhance learning outcomes, improve educational practices, and promote sustainable education.https://ieeexplore.ieee.org/document/10847863/Factors analysisfactor analysis for mixed dataFAMDstudent performancemultiple linear regressionprediction |
| spellingShingle | Mohamed El Jihaoui Oum El Kheir Abra Khalifa Mansouri Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression Analysis IEEE Access Factors analysis factor analysis for mixed data FAMD student performance multiple linear regression prediction |
| title | Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression Analysis |
| title_full | Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression Analysis |
| title_fullStr | Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression Analysis |
| title_full_unstemmed | Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression Analysis |
| title_short | Factors Affecting Student Academic Performance: A Combined Factor Analysis of Mixed Data and Multiple Linear Regression Analysis |
| title_sort | factors affecting student academic performance a combined factor analysis of mixed data and multiple linear regression analysis |
| topic | Factors analysis factor analysis for mixed data FAMD student performance multiple linear regression prediction |
| url | https://ieeexplore.ieee.org/document/10847863/ |
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