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Computational Modeling of Educational Theory in Low-Socioeconomic Contexts

Paper · 2026 · Jadon Swearingen, Mustafa Ocal, Md Tarique Hasan Khan et al.

Summary

Students from low-SES backgrounds face systemic obstacles in higher education that surveys and institutional records capture poorly, and computer science has contributed little to studying them. The authors use three educational frameworks as computational coding schemes: Freire's Critical Pedagogy, Bronfenbrenner's Ecological Systems Theory, and Bourdieu's Theory of Capital and Habitus. They apply all three to a shared corpus of 387 Reddit narratives (Abdelgaber et al., 2025) in which students describe hardships and how they coped. GPT-4.1 tags each narrative with theory-derived categories such as agency, ecological system level, and forms of capital. In parallel, TimeML-style temporal annotation and the TLEX algorithm, which solves timelines as a constraint satisfaction problem, turn posts into ordered event sequences. These sequences show when barriers appear, accumulate, or resolve. Running all three theories on one corpus allows the first direct comparison of their explanatory reach. It also highlights moments where support could be most timely. The supplied text omits the detailed category frequencies. The authors note that cross-post timelines and fragmented within-post event chains remain weak points.

Key points

  • Corpus: 387 English narratives from U.S.-focused subreddits (r/college, r/ApplyingTo-College, r/financialaid, r/fafsa). It was built semi-automatically from 128 hand-labeled seed posts using classifier-based candidate selection, clustering-based outlier removal, and human validation. The data is public on GitHub (NahedAbdelgaber/NLP-for-Social-Science).
  • Theory coding: GPT-4.1 performs the theoretical coding, guided by prompts that define each construct. A second GPT version handles other subtasks. Human validation, label-stability checks, and Cohen's kappa agreement are planned but not yet done.
  • Timeline extraction: TimeML annotations become a temporal constraint graph, and TLEX resolves it with a CSP solver. This addresses a known weakness of automatic TimeML pipelines (TARSQI, CAEVO, ClearTK, CATENA): roughly one-third of their graphs are temporally inconsistent (Ocal et al., 2022).
  • Stated limitations: the dataset is small and Reddit-biased. LLM labels only approximate expert judgment, and the model often confuses "absent" with "deficit" capital. Cross-post ordering suffers from missing dates.
  • Contributions: theory constructs operationalized as measurable features, a temporal view of capital and barriers, a comparative assessment of the three frameworks, and publicly released code.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Florida International UniversityMiami · United States8 works · 777 citations🎓Southern Methodist UniversityDallas · United States4 works · 63 citations

Also on the byline: Tarleton State University.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSociology and Political Science · 577 worksGeneral Social Sciences · 227 worksEducation · 203 works

Cite

Cite as Jadon Swearingen, Mustafa Ocal, Md Tarique Hasan Khan and Labiba Jahan. 2026. Computational Modeling of Educational Theory in Low-Socioeconomic Contexts. Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.nlp4dh-1.25

@inproceedings{swearingen2026computational,
  title = {{Computational Modeling of Educational Theory in Low-Socioeconomic Contexts}},
  author = {Jadon Swearingen, Mustafa Ocal, Md Tarique Hasan Khan and Labiba Jahan},
  booktitle = {Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities},
  year = {2026},
  doi = {10.18653/v1/2026.nlp4dh-1.25},
  url = {https://doi.org/10.18653/v1/2026.nlp4dh-1.25}
}

References

34 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Nahed Abdelgaber, Labiba Jahan, Arham Vinit Doshi, Rishi Suri, Hamza Reza Pavel, and Jia Zhang. 2025. Bridging the socioeconomic gap in education: A hybrid ai and human annotation approach. In Proceedings of the 29th Conference on Computational Natural Language Learning, pages 348–364.
  2. Md. Tarique Hasan Khan, Labiba Jahan, and Mustafa Ocal. 2026. An inflection point in equity research: Leveraging ai and educational theory to decode the socioeconomic gap in engineering education. In Proceedings of the ASEE Gulf-Southwest Annual Conference. American Society for Engineering Education… find
  3. Steven Bethard. 2013. Cleartk-timeml: A minimalist approach to tempeval 2013. In Second joint conference on lexical and computational semantics (* SEM), volume 2: proceedings of the seventh international workshop on semantic evaluation (SemEval 2013), pages 10–14.
  4. Sang Min Lee, M Harry Daniels, Ana Puig, Rebecca A Newgent, and Suk Kyung Nam. 2008. A data-based model to predict postsecondary educational attainment of low-socioeconomic-status students. Professional School Counseling, 11(5):2156759X0801100504. find
  5. Pierre Bourdieu. 1986. The forms of capital. In John Richardson, editor, Handbook of Theory and Research for the Sociology of Education, pages 241– 258. Greenwood Press. find
  6. Shixia Liu, Michelle X. Zhou, Shimei Pan, Yangqiu Song, Weihong Qian, Weijia Cai, and Xiaoxiao Lian. 2012. Tiara: Interactive, topic-based visual text summarization and analysis. ACM Trans. Intell. Syst. Technol., 3(2):25:1–25:28. find
  7. Urie Bronfenbrenner. 1979. The ecology of human development: Experiments by nature and design. Harvard university press. find
  8. Nathanael Chambers, Taylor Cassidy, Bill McDowell, and Steven Bethard. 2014. Dense event ordering with a multi-pass architecture. Transactions of the Association for Computational Linguistics, 2:273– 284. find
  9. Linguistic Data Consortium and 1 others. 2002. The aquaint corpus of english news text. find
  10. Alon Eirew, Kfir Bar, and Ido Dagan. 2025. Beyond pairwise: Global zero-shot temporal graph generation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 31428–31446. find
  11. Engineering Education–Workforce Continuum Committee. 2019. Understanding the educational and career pathways of engineers. National Academies Press. find
  12. Paulo Freire. 2020. Pedagogy of the oppressed. In Toward a sociology of education, pages 374–386. Routledge. find
  13. Ella R Kahu and Catherine Picton. 2022. Using photo elicitation to understand first-year student experiences: Student metaphors of life, university and learning. Active Learning in Higher Education, 23(1):35– 47. find
  14. Motti Kelbessa, Ilyas Jamil, and Labiba Jahan. 2022. Impacts of low socio-economic status on educational outcomes: A narrative based analysis. In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pages 59–69. find
  15. Motti Kelbessa, Estephanos Jebessa, and Labiba Jahan. 2024. Addressing educational inequalities of low ses students: Leveraging natural language processing for impact. In Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA ’24, page 388… find
  16. Julie P Martin, Denise R Simmons, and Shirley L Yu. 2013. The role of social capital in the experiences of hispanic women engineering majors. Journal of Engineering Education, 102(2):227–243. find
  17. Kerry L Meyers, Matthew W Ohland, Alice L Pawley, Stephen E Silliman, and Karl A Smith. 2012. Factors relating to engineering identity. Global Journal of Engineering Education, 14(1):119–131. find
  18. Paramita Mirza and Sara Tonelli. 2016. Catena: Causal and temporal relation extraction from natural language texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 64–75. find
  19. Borja Navarro-Colorado and Estela Saquete. 2016. Cross-document event ordering through temporal, lexical and distributional knowledge. Know.-Based Syst., 110(C):244–254. find
  20. Mustafa Ocal, Adrian Perez, Antonela Radas, and Mark Finlayson. 2022. Holistic evaluation of automatic timeml annotators. In Proceedings of the thirteenth language resources and evaluation conference, pages 1444–1453. find
  21. Mustafa Ocal, Akul Singh, Jared Hummer, Antonela Radas, and Mark Finlayson. 2023. jtlex: a java library for timeline extraction. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 27–34. find
  22. Mustafa Ocal, Ning Xie, and Mark A Finlayson. 2026. Tlex: an efficient method for extracting exact timelines from timeml temporal graphs. Language Resources and Evaluation, 60(1):10. find
  23. Matthew W Ohland, Catherine E Brawner, Michelle M Camacho, Richard A Layton, Russell A Long, Susan M Lord, and Mara H Wasburn. 2011. Race, gender, and measures of success in engineering education. Journal of engineering education, 100(2):225– 252. find
  24. OpenAI. 2025a. Introducing gpt-4.1 in the api. https://openai.com/index/gpt-4-1/. Accessed 2026-05-08. ↗
  25. OpenAI. 2025b. Introducing gpt-5.2. https://openai.com/index/introducing-gpt-5-2/. Accessed 2026-05-08. ↗
  26. Donna Riley. 2022. Engineering and social justice. In Engineering and social justice, pages 47–106. Springer. find
  27. Laura Robinson. 2020. The stem selfing process: Nondigital and digital determinants of aspirational stem futures. American Behavioral Scientist, 64(7):950–972. find
  28. Susan C Saegert, Nancy E Adler, Heather E Bullock, Ana Mari Cauce, William Ming Liu, and Karen F Wyche. 2006. Report of the apa task force on socioeconomic status. Retrieved from the American Psychological Association website. find
  29. E. Saquete, P. Martínez-Barco, R. Muñoz, and J. L. Vicedo. 2004. Splitting complex temporal questions for question answering systems. In Proceedings of the 42Nd Annual Meeting on Association for Computational Linguistics, ACL ’04, Stroudsburg, PA, USA. Association for Computational Linguistics. find
  30. Roser Sauri, Jessica Littman, Robert Gaizauskas, Andrea Setzer, and James Pustejovsky. 2006. TimeML annotation guidelines, version 1.2.1. https://catalog.ldc.upenn.edu/docs/ LDC2006T08/timeml_annguide_1.2.1.pdf. ↗
  31. Stephen Secules, Cassandra McCall, Joel Alejandro Mejia, Chanel Beebe, Adam S Masters, Matilde L. Sánchez-Peña, and Martina Svyantek. 2021. Positionality practices and dimensions of impact on equity research: A collaborative inquiry and call to the community. Journal of Engineering Education, 110(1)… find
  32. Marc Verhagen, Inderjeet Mani, Roser Saurí, Jessica Littman, Robert Knippen, Seok Bae Jang, Anna Rumshisky, John Phillips, and James Pustejovsky. 2005. Automating temporal annotation with tarsqi. In ACL, pages 81–84. find
  33. Jing Wang and Jeremy C Weiss. 2025. A large-language model framework for relative timeline extraction from pubmed case reports. ArXiv, pages arXiv–2504. find
  34. Tara J Yosso*. 2005. Whose culture has capital? a critical race theory discussion of community cultural wealth. Race ethnicity and education, 8(1):69–91. find

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