Addressing Educational Inequalities of Low SES Students: Leveraging Natural Language Processing for Impact

Socioeconomic status (SES) provides a framework for understanding an individual's work experience and socioeconomic position relative to others, considering factors such as income, education, and occupation. Low-SES significantly affects students and their education worldwide. In the United States, students from various ethnic and racial minorities, such as Hispanic, Latino, and African American populations, experience physical and psychological challenges due to low quality of life. Extensive research in various fields, particularly the social sciences, has examined diverse issues for students with low SES. Despite the profound potential of computing, particularly in natural language processing (NLP), to improve the quality of life for these individuals, NLP has yet to make substantial contributions to addressing these inequalities. In this paper, we highlighted the limited contribution of NLP compared to other disciplines in understanding and addressing the problems of low-SES students. Then, we discuss how to bridge this gap by developing NLP tools and techniques from the narratives of low-SES students, including factor and cause-effect analysis, building a knowledge graph, developing helpful apps specifically designed for them. Accomplishing this objective would represent a significant step forward in enhancing the well-being of students from low socioeconomic backgrounds. Additionally, we manually created and published a data set consisting of 74 posts and comments from Reddit for the NLP community to conduct proposed research on low-SES students. Data may be downloaded from https://github.com/NLP-in-the-Social-Sciences/Paper-114-PETRA-2024.