Understanding the Disparities in Mathematics Performance: An Interpretability-Based Examination
- URL: http://arxiv.org/abs/2502.19424v1
- Date: Wed, 29 Jan 2025 00:44:01 GMT
- Title: Understanding the Disparities in Mathematics Performance: An Interpretability-Based Examination
- Authors: Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez,
- Abstract summary: This study aims to unravel the complex factors contributing to educational disparities in Mathematics performance.<n>Students from lower socioeconomic backgrounds possess fewer books and demonstrate lower performance in Mathematics.<n>Gender also emerged as a determinant, with females contributing differently to performance levels across the spectrum.
- Score: 0.5266869303483376
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Problem. Educational disparities in Mathematics performance are a persistent challenge. This study aims to unravel the complex factors contributing to these disparities among students internationally, with a focus on the interpretability of the contributing factors. Methodology. Utilizing data from the Programme for International Student Assessment (PISA), we conducted rigorous preprocessing and variable selection to prepare for applying binary classification interpretability models. These models were trained using the Stratified K-Fold technique to ensure balanced representation and assessed using six key metrics. Solution. By applying interpretability models such as Shapley Additive Explanations (SHAP) analysis, we identified critical factors impacting student performance, including reading accessibility, critical thinking skills, gender, and geographical location. Results. Our findings reveal significant disparities linked to resource availability, with students from lower socioeconomic backgrounds possessing fewer books and demonstrating lower performance in Mathematics. The geographical analysis highlighted regional educational disparities, with certain areas consistently underperforming in PISA assessments. Gender also emerged as a determinant, with females contributing differently to performance levels across the spectrum. Conclusion. The study provides insights into the multifaceted determinants of student Mathematics performance and suggests potential avenues for future research to explore global interpretability models and further investigate the socioeconomic, cultural, and educational factors at play.
Related papers
- Gender assignment in doctoral theses: revisiting Teseo with a method based on cultural consensus theory [0.0]
This study critically evaluates gender assignment methods within academic contexts.<n>The research introduces nomquamgender, a cultural consensus-based method, and applies it to Teseo, a Spanish dissertation database.
arXiv Detail & Related papers (2025-01-20T15:22:01Z) - Bridging the Gap: Protocol Towards Fair and Consistent Affect Analysis [24.737468736951374]
The increasing integration of machine learning algorithms in daily life underscores the critical need for fairness and equity in their deployment.
Existing databases and methodologies lack uniformity, leading to biased evaluations.
This work addresses these issues by analyzing six affective databases, annotating demographic attributes, and proposing a common protocol for database partitioning.
arXiv Detail & Related papers (2024-05-10T22:40:01Z) - Sensitivity, Performance, Robustness: Deconstructing the Effect of
Sociodemographic Prompting [64.80538055623842]
sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give.
We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
arXiv Detail & Related papers (2023-09-13T15:42:06Z) - Investigating Fairness Disparities in Peer Review: A Language Model
Enhanced Approach [77.61131357420201]
We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs)
We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date.
We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
arXiv Detail & Related papers (2022-11-07T16:19:42Z) - Evaluation of group fairness measures in student performance prediction
problems [12.502377311068757]
We evaluate different group fairness measures for student performance prediction problems on various educational datasets and fairness-aware learning models.
Our study shows that the choice of the fairness measure is important, likewise for the choice of the grade threshold.
arXiv Detail & Related papers (2022-08-22T22:06:08Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Auditing Fairness and Imputation Impact in Predictive Analytics for
Higher Education [0.0]
There are two major barriers to the adoption of predictive analytics in higher education.
The lack of democratization in deployment and the potential to exacerbate inequalities are cited.
arXiv Detail & Related papers (2021-09-13T05:08:40Z) - Towards Equity and Algorithmic Fairness in Student Grade Prediction [2.9189409618561966]
This work addresses equity of educational outcome and fairness of AI with respect to race.
We trial several strategies for both label and instance balancing to attempt to minimize differences in algorithm performance with respect to race.
We find that an adversarial learning approach, combined with grade label balancing, achieved by far the fairest results.
arXiv Detail & Related papers (2021-05-14T01:12:01Z) - Through the Data Management Lens: Experimental Analysis and Evaluation
of Fair Classification [75.49600684537117]
Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness.
We contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, and stability.
Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance.
arXiv Detail & Related papers (2021-01-18T22:55:40Z) - Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type.
Recent literature has explored representation learning to achieve this goal.
We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
arXiv Detail & Related papers (2020-10-23T19:06:03Z) - Accurate and Robust Feature Importance Estimation under Distribution
Shifts [49.58991359544005]
PRoFILE is a novel feature importance estimation method.
We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
arXiv Detail & Related papers (2020-09-30T05:29:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.