Abstract
The concept of “human-in-the-loop” (HITL) has gained increased attention in the field of educational recommendation systems (ERS). ERS aims to provide personalized learning experiences by suggesting relevant learning resources or activities to individual learners. HITL in ERS involves incorporating human intervention and decision making into the recommendation process, leveraging the unique capabilities of both human and machine algorithms. This approach recognizes the importance of human expertise, preferences, and context in the learning process and seeks to enhance the effectiveness and relevance of recommendations by actively involving users in the recommendation process. In this paper, we explore the concept of HITL in ERS by performing a systematic review of the literature. The systematic review examines the key components of HITL in ERS, including the integration of human feedback, the role of machine learning algorithms, and the impact on the overall recommendation process. The findings of this systematic review provide insights into the current state of HITL in educational recommendation systems and highlight areas for future research and development in this promising field.
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References
Kantor, P.B., Ricci, F., Rokach, L., Shapira, B.: Recommender systems handbook. Springer, Heidelberg, Germany (2011)
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). http://prisma-statement.org/. Last accessed 21 Mar 2013
Arambepola, N., Munasinghe, L.: Human in the loop design for intelligent interactive systems: a systematic review. In: Proceedings of the International Conference on Applied and Pure Sciences (ICAPS 2021-Kelaniya), Vol. 1, Faculty of Science, University of Kelaniya, Sri Lanka, pp. 225 (2021)
Holzinger,A., Calero Valdez, A., Ziefle, M.: Towards interactive recommender systems with the doctor-in-the-loop (2016). https://doi.org/10.18420/muc2016-ws11-0001
Grønsund, T., Aanestad, M.: Augmenting the algorithm: emerging humanin-the-loop work configurations. J. Strateg. Inf. Syst. 29, 101614 (2020)
Fu, U., Xian, Y., Zhu, Y., Xu, S., Li, Z., de Melo, G., Zhang, Y.: HOOPS: human-in-the-loop graph reasoning for conversational recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’21), pp. 2415–2421. Association for Computing Machinery, New York, NY (2021). https://doi.org/10.1145/3404835.3463247
Gao, F., Luo, T., Zhang, K.: Tweeting for learning: A critical analysis of research on microblogging in education published in 2008–2011. Br. J. Edu. Technol. 43(5), 783–801 (2012). https://doi.org/10.1111/j.1467-8535.2012.01357.x
Fotopoulou, E., Zafeiropoulos, A., Feidakis, M., Metafas, D., Papavassiliou, S.: An interactive recommender system based on reinforcement learning for improving emotional competences in educational groups. In: Kumar, V., Troussas, C. (Eds.), Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science, Vol. 12149. Springer (2020). https://doi.org/10.1007/978-3-030-49663-0_29
Fu, Z., Xian, Y., Geng, S., de Melo, G., Zhang, Y.: Popcorn: human-inthe-loop popularity debiasing in conversational recommender systems. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM’21), pp. 494–503. Association for Computing Machinery, New York, NY (2021). https://doi.org/10.1145/3459637.3482461
Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., et al.: Human in-the-loop machine learning: a state of the art. Artif. Intell. Rev. 56(3), 3005–3054 (2023). https://doi.org/10.1007/s10462-022-10246-w
Oktavia, T., Sujarwo, S.: Interactive recommender system for identifying learning partners. Int. J. Emerg. Technol. Adv. Eng. 11, 72–77 (2021). https://doi.org/10.46338/ijetae0621_09
Rodríguez-Triana, M., Prieto, L., Martínez-Monés, A., Asensio-Pérez, J.I., Dimitriadis, Y.: The teacher in the loop: customizing multimodal learning analytics for blended learning. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK’18), pp. 417–426. ACM (2018). https://doi.org/10.1145/3170358.3170364
Tejeda-Lorente, A., Bernabe-Moreno, J., Porcel, C., Galindo-Moreno, P., HerreraViedma, E.: A dynamic recommender system as reinforcement for personalized education by a fuzzy linguistic web system. Procedia Comp. Sci. 55, 1143–1150 (2015). https://doi.org/10.1016/j.procs.2015.07.084
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The research presented in this publication has been carried out with the support of the National Agency for Research and Innovation (ANII) in Uruguay, under the code FSED_2_2021_1_169701.
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Viola, M., de Queiroz, D., Motz, R. (2023). Is the Human-in-the-Loop Concept Applied in Educational Recommender Systems?. In: Mesquita, A., Abreu, A., Carvalho, J.V., Santana, C., de Mello, C.H.P. (eds) Perspectives and Trends in Education and Technology. ICITED 2023. Smart Innovation, Systems and Technologies, vol 366. Springer, Singapore. https://doi.org/10.1007/978-981-99-5414-8_61
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