## 3 Methodology
### Ethical Compliance, Open Science Principles, and Reproducibility  
This study rigorously adheres to the highest ethical standards and embraces the principles of open science to ensure maximal transparency and reproducibility. The entire study protocol, including data collection and analysis procedures, received stringent approval from the University Ethics Committee (Ref: #2024-EDU-AI-03), underscoring our commitment to responsible research. Furthermore, all student data utilized were meticulously anonymized in full compliance with the strict privacy regulations stipulated by the Family Educational Rights and Privacy Act (FERPA) and the General Data Protection Regulation (GDPR), thereby safeguarding student confidentiality and data security.  
In a concerted effort to foster research transparency and enable complete reproducibility, the foundational UCI Machine Learning Repository (publicly accessible at [[https://archive.ics.uci.edu/ml/datasets/Student+Performance](https://archive.ics.uci.edu/ml/datasets/Student+Performance)]([https://archive.ics.uci.edu/ml/datasets/Student+Performance](https://archive.ics.uci.edu/ml/datasets/Student+Performance)), accessed on 1 July 2025) was utilized. Crucially, all developed source code, comprehensive model artifacts, and detailed interpretability outputs are made freely available at our dedicated public GitHub repository ([[https://github.com/kdrksgn/fairsynedu](https://github.com/kdrksgn/fairsynedu)]([https://github.com/kdrksgn/fairsynedu](https://github.com/kdrksgn/fairsynedu)), accessed on 1 July 2025). The code-base was developed using Python version 3.10. This commitment to open-source sharing not only allows for independent verification and replication of our findings but also serves to accelerate collaborative progress and democratic innovation within the educational AI research community.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy** & **DP** & **EO** \\ \hline Logistic Regression & 0.886 & 0.023 & 0.074 \\ Random Forest & 0.873 & 0.002 & 0.041 \\ XGBoost & 0.873 & 0.012 & 0.084 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fairness evaluation of classification models according to demographic parity (DP) and Equalized Odds (EO) with gender as the sensitive attribute.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \(\mathbf{\lambda}\) & **Accuracy** & **AUC** & \(\left|\mathbf{DP}\right|\) & \(\left|\mathbf{EO}\right|\) \\ \hline
0.1 & 0.684 & 0.570 & 0.049 & 0.080 \\
0.3 & 0.608 & 0.583 & 0.089 & 0.170 \\
0.5 & 0.646 & 0.606 & 0.320 & 0.290 \\
0.7 & 0.658 & 0.623 & 0.047 & 0.146 \\
0.9 & 0.595 & 0.625 & 0.448 & 0.457 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adversarial debiasing modelinin farkli \(\lambda\) degerleri altunda test seti üzerindeki basán (Accuracy, AUC) ve adalet (DP and EO) metrikleri.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy** & **F1 Score** & **AUC** \\ \hline Logistic Regression & 0.775 & 0.867 & 0.671 \\ Random Forest & 0.789 & 0.876 & 0.665 \\ XGBoost & 0.780 & 0.868 & 0.665 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification performance metrics (accuracy, F1 score, and AUC) for all the models.  
Extended Fairness Audit with Socioeconomic Attributes  
In addition to gender and school type, we extended the fairness analysis to include attributes that reflect students' socioeconomic background. These include parental education levels (\"Medu\" and \"Fedu\"), parental cohabitation status (\"Pstatus\"), and family size (\"famsize\"). These variables were binarized and evaluated using demographic parity and Equalized Odds metrics via the AIF360 library. This allowed us to assess model bias beyond traditional groupings and uncover fairness gaps related to socioeconomic context. Our extended fairness audit, encompassing socioeconomic and demographic features, follows recommendations outlined in recent surveys on robust bias assessment [19,30].