The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Assessing Algorithmic Fairness in Automatic Classifiers of Educational Forum Posts
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Several studies highlight the risks of underperforming or biased AI models, which can negatively impact human learning and perpetuate systemic biases.
“Several studies highlight the risks of underperforming or biased AI models, which can negatively impact human learning and perpetuate systemic biases, such as gender, racial, and social class biases [87,88].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [87]
Two studies have suggested that potential bias and discrimination issues may occur if adopting a model that is accurate but unfair.
“Apart from issues with adopting inaccurate models, two studies have suggested that potential bias and discrimination issues may occur if adopting a model that is accurate but unfair (Merine & Purkayastha, 2022;Sha et al., 2021).”
From Practical and ethical challenges of large language models in education: A systematic scoping review (2024) · cites this paper as Sha et al., 2021
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854
Predictive biases in natural language processing models: A conceptual framework and overview
Shah, D. S., Schwartz, H. A., & Hovy, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.468 · Cited by 241
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Research works printed but not linked: 47
- Almatrafi, O., Johri, A., Rangwala, H.: Needle in a haystack: identifying learner posts that require urgent response in MOOC discussion forums. Comput. Educ. 118, 1–9 (2018) · DOI
- Alrajhi, L., Alharbi, K., Cristea, A.I.: A multidimensional deep learner model of urgent instructor intervention need in MOOC forum posts. In: Intelligent Tutoring Systems, pp. 226–236. Springer International Publishing, New York City (2020) · DOI
- Bakharia, A.: Towards cross-domain MOOC forum post classification. In: Learning@Scale, pp. 253–256 (2016) · DOI
- Barbosa, G., et al.: Towards automatic cross-language classification of cognitive presence in online discussions. In: LAK, pp. 605–614 (2020) · DOI
- Bird, S.: Nltk: the natural language toolkit. In: Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions, pp. 69–72 (2006) · DOI · Paper page
- Caines, A., Pastrana, S., Hutchings, A., Buttery, P.J.: Automatically identifying the function and intent of posts in underground forums. Crime Sci. 7(1), 19 (2018) · DOI
- Chen, J., Feng, J., Sun, X., Liu, Y.: Co-training semi-supervised deep learning for sentiment classification of MOOC forum posts. Symmetry 12(1), 8 (2020) · DOI
- Clavié, B., Gal, K.: Edubert: Pretrained deep language models for learning analytics (2019). arXiv preprint arXiv:1912.00690 · arXiv
- Cui, Y., Wise, A.F.: Identifying content-related threads in MOOC discussion forums. In: Learning@Scale, pp. 299–303 (2015) · DOI
- Doroudi, S., Brunskill, E.: Fairer but not fair enough on the equitability of knowledge tracing. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pp. 335–339. LAK19, Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3303772.3303838 · DOI
- Duran, R., Haaranen, L., Hellas, A.: Gender differences in introductory programming: comparing MOOCs and local courses. In: Proceedings of the 51st ACM Technical Symposium on Computer Science Education, pp. 692–698 (2020) · DOI
- Feng, L., Liu, G., Luo, S., Liu, S.: A transferable framework: classification and visualization of MOOC discussion threads. In: International Conference on Neural Information Processing, pp. 377–384. Springer (2017) · DOI
- Ferreira, M., Rolim, V., Ferreira Mello, R., Lins, R.D., Chen, G., Gašević, D.: Towards automatic content analysis of social presence in transcripts of online discussions. In: LAK, pp. 141–150 (2020) · DOI
- Gardner, J., Brooks, C., Baker, R.: Evaluating the fairness of predictive student models through slicing analysis. In: LAK, pp. 225–234 (2019) · DOI
- Guo, P.J.: Non-native English speakers learning computer programming: barriers, desires, and design opportunities. In: Proceedings of the 2018 CHI conference on human factors in computing systems, pp. 1–14 (2018) · DOI
- Guo, S.X., Sun, X., Wang, S.X., Gao, Y., Feng, J.: Attention-based character-word hybrid neural networks with semantic and structural information for identifying of urgent posts in MOOC discussion forums. IEEE Access 7, 120522–120532 (2019) · DOI
- Hew, K.F., Cheung, W.S.: Students’ and instructors’ use of massive open online courses (MOOCs): motivations and challenges. Educ. Res. Rev. 12, 45–58 (2014) · DOI
- Hew, K.F., Hu, X., Qiao, C., Tang, Y.: What predicts student satisfaction with MOOCs: a gradient boosting trees supervised machine learning and sentiment analysis approach. Comput. Educ. 145, 103724 (2020) · DOI
- Hone, K.S., El Said, G.R.: Exploring the factors affecting MOOC retention: a survey study. Comput. Educ. 98, 157–168 (2016) · DOI
- Jansen, R.S., van Leeuwen, A., Janssen, J., Conijn, R., Kester, L.: Supporting learners’ self-regulated learning in massive open online courses. Comput. Educ. 146, 103771 (2020) · DOI
- Khan, A., et al.: Machine learning approach for answer detection in discussion forums: an application of big data analytics. Sci. Program. 2020, (2020) · DOI
- Kovanović, V., Joksimović, S., Waters, Z., Gašević, D., Kitto, K., Hatala, M., Siemens, G.: Towards automated content analysis of discussion transcripts: a cognitive presence case. In: LAK, pp. 15–24 (2016) · DOI
- Loukina, A., Madnani, N., Zechner, K.: The many dimensions of algorithmic fairness in educational applications. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 1–10. Association for Computational Linguistics, Florence, Italy (August 2019). https://doi.org/10.18653/v1/W19-4401, https://www.aclweb.org/anthology/W19-4401 · DOI
- Lui, M., Baldwin, T.: Classifying user forum participants: separating the gurus from the hacks, and other tales of the internet. Proc. Australas. Lang. Technol. Assoc. Workshop 2010, 49–57 (2010)
- Mitra, S.K.: Internationalization of education in India: emerging trends and strategies. Asian Soc. Sci. 6(6), 105 (2010) · DOI
- Moreno-Marcos, P.M., Alario-Hoyos, C., Muñoz-Merino, P.J., Estévez-Ayres, I., Kloos, C.D.: Sentiment analysis in MOOCs: a case study. In: 2018 IEEE Global Engineering Education Conference (EDUCON), pp. 1489–1496. IEEE (2018) · DOI
- Morris, L.V., Finnegan, C., Wu, S.S.: Tracking student behavior, persistence, and achievement in online courses. Internet High. Educ. 8(3), 221–231 (2005) · DOI
- Neto, V., Rolim, V., Ferreira, R., Kovanović, V., Gašević, D., Dueire Lins, R., Lins, R.: Automated analysis of cognitive presence in online discussions written in portuguese. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 245–261. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_19 · DOI
- Ntourmas, A., Avouris, N., Daskalaki, S., Dimitriadis, Y.: Comparative study of two different MOOC forums posts classifiers: analysis and generalizability issues. In: 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–8. IEEE (2019) · DOI
- Probyn, M.: Teachers voices: teachers reflections on learning and teaching through the medium of English as an additional language in South Africa. Int. J. Biling. Educ. Biling. 4(4), 249–266 (2001) · DOI
- Rayyan, S., Seaton, D.T., Belcher, J., Pritchard, D.E., Chuang, I.: Participation and performance in 8.02 x electricity and magnetism: The first physics MOOC from mitx (2013). arXiv preprint arXiv:1310.3173 · arXiv
- Riazy, S., Simbeck, K., Schreck, V.: Fairness in learning analytics: student at-risk prediction in virtual learning environments. In: CSEDU (1), pp. 15–25 (2020) · DOI
- Rossi, L.A., Gnawali, O.: Language independent analysis and classification of discussion threads in Coursera MOOC forums. In: Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014), pp. 654–661. IEEE (2014) · DOI
- Rovai, A.P.: Sense of community, perceived cognitive learning, and persistence in asynchronous learning networks. Internet High. Educ. 5(4), 319–332 (2002) · DOI
- Rovai, A.P.: Facilitating online discussions effectively. Internet High. Educ. 10(1), 77–88 (2007) · DOI
- Sun, C., Li, S., Lin, L.: Thread structure prediction for MOOC discussion forum. In: Che, W., Han, Q., Wang, H., Jing, W., Peng, S., Lin, J., Sun, G., Song, X., Song, H., Lu, Z. (eds.) ICYCSEE 2016. CCIS, vol. 624, pp. 92–101. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2098-8_13 · DOI
- Toven-Lindsey, B., Rhoads, R.A., Lozano, J.B.: Virtually unlimited classrooms: pedagogical practices in massive open online courses. Internet High. Educ. 24, 1–12 (2015) · DOI
- Tsai, Y.S., Perrotta, C., Gašević, D.: Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assess. Eval. High. Educ. 45(4), 554–567 (2020). https://doi.org/10.1080/02602938.2019.1676396 · DOI
- Verstegen, D., Dailey-Hebert, A., Fonteijn, H., Clarebout, G., Spruijt, A.: How do virtual teams collaborate in online learning tasks in a MOOC? Int. Rev. Res. Open Distrib. Learn. 19(4), 1–18 (2018) · DOI
- Wei, X., Lin, H., Yang, L., Yu, Y.: A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification. Information 8(3), 92 (2017) · DOI
- Wise, A.F., Cui, Y., Jin, W., Vytasek, J.: Mining for gold: identifying content-related MOOC discussion threads across domains through linguistic modeling. Internet High. Educ. 32, 11–28 (2017) · DOI
- Wise, A.F., Cui, Y., Vytasek, J.: Bringing order to chaos in MOOC discussion forums with content-related thread identification. In: LAK, pp. 188–197 (2016) · DOI
- Xing, W., Tang, H., Pei, B.: Beyond positive and negative emotions: looking into the role of achievement emotions in discussion forums of MOOCs. Internet High. Educ. 43,100690 (2019) · DOI
- Xu, Y., Lynch, C.F.: What do you want? Applying deep learning models to detect question topics in MOOC forum posts? In: Wood-stock 2018: ACM Symposium on Neural Gaze Detection, pp. 1–6 (2018)
- Yan, S., Kao, H.t., Ferrara, E.: Fair class balancing: Enhancing model fairness without observing sensitive attributes. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1715–1724 (2020) · DOI
- Yang, D., Wen, M., Howley, I., Kraut, R., Rose, C.: Exploring the effect of confusion in discussion forums of massive open online courses. In: Learning@Scale, pp. 121–130 (2015) · DOI
- Yu, R., Li, Q., Fischer, C., Doroudi, S., Xu, D.: Towards accurate and fair prediction of college success: evaluating different sources of student data. In: EDM. pp. 292–301. ERIC (2020)
Cited by
Sorted by citations
Practical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 1,435
“Apart from issues with adopting inaccurate models, two studies have suggested that potential bias and discrimination issues may occur if adopting a model that is accurate but unfair (Sha et al., 2021; Merine and Purkayastha, 2022).”
BackgroundSelf-citationPromises and challenges of generative artificial intelligence for human learning
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02004-5 · Cited by 499
“They can generate text by taking inputs and repeatedly predicting the next word—a form of GenAI.”
Self-citationShould learning analytics models include sensitive attributes? Explaining the why
Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3226474 · Cited by 35
Metadata
- Authors
- Sha, L., Rakovic, M., Whitelock-Wainwright, A., Carroll, D., Yew, V. M., Gasevic, D., & Chen, G.
- Affiliations
- Monash University (AU)
- Corresponding author
- Chen, Guanliang
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Volume
- 12748 LNAI
- Pages
- 381-394
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3165938262
- Cited by
- 34
- References
- 3
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 9 · 2023: 7 · 2022: 6 · 2021: 1
- FWCI
- 5.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- algorithmic fairness, sex bias, English as a second language, data sampling, online learning analytics
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