{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:49:45Z","timestamp":1782478185070,"version":"3.54.5"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032297723","type":"print"},{"value":"9783032297730","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-29773-0_34","type":"book-chapter","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:24:48Z","timestamp":1782476688000},"page":"424-433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["When Features Misrepresent Underrepresented Learners: Auditing Algorithmic Bias with\u00a0Differentially Expressive Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-7898","authenticated-orcid":false,"given":"Jaeyoon","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7920-4510","authenticated-orcid":false,"given":"Shamya","family":"Karumbaiah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"34_CR1","unstructured":"Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning (2006)"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Blodgett, S.L., Barocas, S., Daum\u00e9\u00a0III, H., Wallach, H.: Language (technology) is power: a critical survey of \u201cbias\u201d in NLP. arXiv preprint arXiv:2005.14050 (2020)","DOI":"10.18653\/v1\/2020.acl-main.485"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Brooks, C., Thompson, C.: Predictive modelling in teaching and learning. In: Handbook of Learning Analytics, pp. 61\u201368 (2017)","DOI":"10.18608\/hla17.005"},{"key":"34_CR4","unstructured":"Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pp. 77\u201391. PMLR (2018)"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Choi, J., Karumbaiah, S., Matayoshi, J.: Bias or insufficient sample size? Improving reliable estimation of algorithmic bias for minority groups. In: Proceedings of the 15th International Learning Analytics and Knowledge Conference, pp. 547\u2013557 (2025)","DOI":"10.1145\/3706468.3706540"},{"key":"34_CR6","unstructured":"Cortez, P., Silva, A.M.G.: Using data mining to predict secondary school student performance (2008)"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Kearns, M., Neel, S., Roth, A., Wu, Z.S.: An empirical study of rich subgroup fairness for machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 100\u2013109 (2019)","DOI":"10.1145\/3287560.3287592"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Lee, H., Belitz, C., Nasiar, N., Bosch, N.: XAI reveals the causes of attention deficit hyperactivity disorder (ADHD) bias in student performance prediction. In: Proceedings of the 15th International Learning Analytics and Knowledge Conference, pp. 418\u2013428 (2025)","DOI":"10.1145\/3706468.3706521"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Matthews, J.S., Boomhower, K.M., Ekwueme, C.: Assimilationist, reformist, and sociopolitical phases of school belonging research: a critical race and optimal distinctiveness review. In: Advances in Motivation Science, vol. 11, pp. 171\u2013213. Elsevier (2024)","DOI":"10.1016\/bs.adms.2023.12.001"},{"issue":"6","key":"34_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. (CSUR) 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"4","key":"34_CR11","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1109\/TLT.2018.2883419","volume":"12","author":"PM Moreno-Marcos","year":"2018","unstructured":"Moreno-Marcos, P.M., Alario-Hoyos, C., Mu\u00f1oz-Merino, P.J., Estevez-Ayres, I., Kloos, C.D.: A learning analytics methodology for understanding social interactions in MOOCs. IEEE Trans. Learn. Technol. 12(4), 442\u2013455 (2018)","journal-title":"IEEE Trans. Learn. Technol."},{"key":"34_CR12","unstructured":"Olsen, J.K., Aleven, V., Rummel, N.: Predicting student performance in a collaborative learning environment. International Educational Data Mining Society (2015)"},{"key":"34_CR13","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: Catboost: unbiased boosting with categorical features. In: Advances in Neural Information Processing Systems, p. 31 (2018)"},{"key":"34_CR14","unstructured":"Ribeiro, J., Cardoso, L., Santos, V., Carvalho, E., Carneiro, N., Alves, R.: How reliable and stable are explanations of XAI methods? arXiv preprint arXiv:2407.03108 (2024)"},{"key":"34_CR15","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.compedu.2017.03.003","volume":"110","author":"HB Shapiro","year":"2017","unstructured":"Shapiro, H.B., Lee, C.H., Roth, N.E.W., Li, K., \u00c7etinkaya-Rundel, M., Canelas, D.A.: Understanding the massive open online course (MOOC) student experience: an examination of attitudes, motivations, and barriers. Comput. Educ. 110, 35\u201350 (2017)","journal-title":"Comput. Educ."},{"key":"34_CR16","unstructured":"Sun, T., et al.: Mitigating gender bias in natural language processing: literature review. arXiv preprint arXiv:1906.08976 (2019)"},{"key":"34_CR17","doi-asserted-by":"publisher","first-page":"23792","DOI":"10.1109\/ACCESS.2017.2740980","volume":"5","author":"A Zollanvari","year":"2017","unstructured":"Zollanvari, A., Kizilirmak, R.C., Kho, Y.H., Hern\u00e1ndez-Torrano, D.: Predicting students\u2019 GPA and developing intervention strategies based on self-regulatory learning behaviors. IEEE Access 5, 23792\u201323802 (2017)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29773-0_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:25:05Z","timestamp":1782476705000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29773-0_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"ISBN":["9783032297723","9783032297730"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29773-0_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]},"assertion":[{"value":"27 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seoul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aied-conference.org\/2026","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}