Cognitive Biases in Search as Learning: Bridging Conceptual Foundations and Empirical Research
Resumo
Online search is a key part of how people learn, yet it is not a neutral process. Cognitive biases shape how users search for, select, and interpret information. While the Search as Learning (SAL) field studies how people learn throughout the search process, it has not yet integrated research on cognitive biases. This paper bridges that gap by proposing a conceptual model and an experimental framework that connect SAL with cognitive bias research. We conducted a real-world experiment on confirmation bias, involving learners searching for “The use of AI in education”. It showed how prior beliefs influence search behaviors. This work advances both the theoretical understanding and empirical study of biases in SAL, supporting the development of fairer, more transparent, and educationally effective search technologies.Referências
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Machado, M., de Souza, J. F., and Siqueira, S. W. (2024b). Identifying confirmation bias in a search as learning task: A study on the use of artificial intelligence in education. In Simpósio Brasileiro de Informática na Educação (SBIE), pages 1208–1221. SBC.
Machado, M. d. O. C., de Alcantara Gimenez, P. J., and Siqueira, S. W. M. (2020). Raising the dimensions and variables for searching as a learning process: a systematic mapping of the literature. Simpósio Brasileiro de Informática na Educação (SBIE), pages 1393–1402.
Murphy, J., Hofacker, C., and Mizerski, R. (2006). Primacy and recency effects on clicking behavior. Journal of computer-mediated communication, 11(2):522–535.
Rieger, A., Draws, T., Theune, M., and Tintarev, N. (2021). This item might reinforce your opinion: Obfuscation and labeling of search results to mitigate confirmation bias. In Proceedings of the 32nd ACM Conference on Hypertext and Social Media, pages 189–199.
Rieh, S. Y., Collins-Thompson, K., Hansen, P., and Lee, H.-J. (2016). Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science, 42(1):19–34.
Russo, L. and Russo, S. (2020). Search engines, cognitive biases and the man–computer interaction: a theoretical framework for empirical researches about cognitive biases in online search on health-related topics. Medicine, Health Care and Philosophy, 23(2):237–246.
Shokouhi, M., White, R., and Yilmaz, E. (2015). Anchoring and adjustment in relevance estimation. In Proceedings of the 38th International ACM SIGIR Conference on research and development in information retrieval, pages 963–966.
Tversky, A. and Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4):297–323.
Vakkari, P. (2016). Searching as learning: A systematization based on literature. Journal of Information Science, 42(1):7–18.
Von Hoyer, J., Hoppe, A., Kammerer, Y., Otto, C., Pardi, G., Rokicki, M., Yu, R., Dietze, S., Ewerth, R., and Holtz, P. (2022). The search as learning spaceship: Toward a comprehensive model of psychological and technological facets of search as learning. Frontiers in Psychology, 13:827748.
White, R. W. and Horvitz, E. (2015). Belief dynamics and biases in web search. ACM Transactions on Information Systems (TOIS), 33(4):1–46.
Azzopardi, L. (2021). Cognitive biases in search: a review and reflection of cognitive biases in information retrieval. In Proceedings of the 2021 conference on human information interaction and retrieval, pages 27–37.
Baeza-Yates, R. (2020). Bias in search and recommender systems. In Proceedings of the 14th ACM Conference on Recommender Systems, RecSys ’20, page 2, New York, NY, USA. Association for Computing Machinery.
Freire, P. (2013). Pedagogia do Oprimido. Paz e Terra, Rio de Janeiro. Gluck, J. C. (2020). How searches fail: Cognitive bias in literature searching. Journal of Hospital Librarianship, 20(1):27–37.
Gomroki, G., Behzadi, H., Fattahi, R., and Salehi Fadardi, J. (2023). Identifying effective cognitive biases in information retrieval. Journal of Information Science, 49(2):348–358.
Haider, J. and Sundin, O. (2019). Invisible Search and Online Search Engines: The Ubiquity of Search in Everyday Life. Routledge.
Hansen, P. and Rieh, S. Y. (2016). Editorial: Recent advances on searching as learning: An introduction to the special issue.
Hobbs, R. (2010). Digital and Media Literacy: A Plan of Action. Aspen Institute, New York.
Kahneman, D. (2011). Thinking, fast and slow. macmillan. Knobloch-Westerwick, S., Johnson, B. K., and Westerwick, A. (2015). Confirmation bias in online searches: Impacts of selective exposure before an election on political attitude strength and shifts. Journal of Computer-Mediated Communication, 20(2):171–187.
Machado, M., Assis, E. C., Souza, J. F., and Siqueira, S. W. M. (2024a). A framework to support experimentation in the context of cognitive biases in search as a learning process. In Proceedings of the 20th Brazilian Symposium on Information Systems, SBSI ’24, New York, NY, USA. Association for Computing Machinery.
Machado, M., de Souza, J. F., and Siqueira, S. W. (2024b). Identifying confirmation bias in a search as learning task: A study on the use of artificial intelligence in education. In Simpósio Brasileiro de Informática na Educação (SBIE), pages 1208–1221. SBC.
Machado, M. d. O. C., de Alcantara Gimenez, P. J., and Siqueira, S. W. M. (2020). Raising the dimensions and variables for searching as a learning process: a systematic mapping of the literature. Simpósio Brasileiro de Informática na Educação (SBIE), pages 1393–1402.
Murphy, J., Hofacker, C., and Mizerski, R. (2006). Primacy and recency effects on clicking behavior. Journal of computer-mediated communication, 11(2):522–535.
Rieger, A., Draws, T., Theune, M., and Tintarev, N. (2021). This item might reinforce your opinion: Obfuscation and labeling of search results to mitigate confirmation bias. In Proceedings of the 32nd ACM Conference on Hypertext and Social Media, pages 189–199.
Rieh, S. Y., Collins-Thompson, K., Hansen, P., and Lee, H.-J. (2016). Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science, 42(1):19–34.
Russo, L. and Russo, S. (2020). Search engines, cognitive biases and the man–computer interaction: a theoretical framework for empirical researches about cognitive biases in online search on health-related topics. Medicine, Health Care and Philosophy, 23(2):237–246.
Shokouhi, M., White, R., and Yilmaz, E. (2015). Anchoring and adjustment in relevance estimation. In Proceedings of the 38th International ACM SIGIR Conference on research and development in information retrieval, pages 963–966.
Tversky, A. and Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4):297–323.
Vakkari, P. (2016). Searching as learning: A systematization based on literature. Journal of Information Science, 42(1):7–18.
Von Hoyer, J., Hoppe, A., Kammerer, Y., Otto, C., Pardi, G., Rokicki, M., Yu, R., Dietze, S., Ewerth, R., and Holtz, P. (2022). The search as learning spaceship: Toward a comprehensive model of psychological and technological facets of search as learning. Frontiers in Psychology, 13:827748.
White, R. W. and Horvitz, E. (2015). Belief dynamics and biases in web search. ACM Transactions on Information Systems (TOIS), 33(4):1–46.
Publicado
24/11/2025
Como Citar
MACHADO, Marcelo; SOUZA, Jairo F. de; SIQUEIRA, Sean W. M..
Cognitive Biases in Search as Learning: Bridging Conceptual Foundations and Empirical Research. In: CONCURSO ALEXANDRE DIRENE (CTD-IE) - TESES DE DOUTORADO - CONGRESSO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (CBIE), 14. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 47-60.
DOI: https://doi.org/10.5753/cbie_estendido.2025.13760.
