Unraveling the Resistance and Strategies of Human Tutors to Engaging Students in E-Learning

Authors

  • Aluisio Pereira UFPE
  • Alex Sandro Gomes Universidade Federal de Pernambuco, UFPE
  • Tiago Thompsen Primo Universidade Federal de Pelotas, UFPel

DOI:

https://doi.org/10.14571/brajets.v16.n3.551-566

Keywords:

human tutors, intelligent tutoring systems, students, e-learning

Abstract

Artificial Intelligence (AI) advances in several domains, but in education there is still a lack of solutions that combine the advantages of AI with the performance of human tutors. This article aims to identify contingencies in the role of human tutors in supporting student engagement in online and blended learning. A digital ethnographic study was conducted to analyze the dimensions of tutoring. With re-enactment techniques and interviews to collect the difficulties of acting, from the perceptions of three tutors who participate in tutoring in a specific teaching-learning context mediated by Educational Social Network (ESN) platform. The results highlight the difficulties, outline the skills and conceptually represent the needs. It is concluded that the valuation of the interpersonal facilitates the creation of relationships of trust, which, in turn, contributes to the reduction of difficulties in tutoring. This manifests itself as a promising approach for Intelligent Tutoring Systems (ITS) that complement human skills in action.

References

Alhazmi, A. K., Alhammadi, F., Zain, A. A., Kaed, E., & Ahmed, B. (2023). AI’s Role and Application in Education: Systematic Review. Intelligent Sustainable Systems: Selected Papers of WorldS4 2022, 1, 1-14. https://doi.org/10.1007/978-981-19-7660-5_1

AlShaikh, F., & Hewahi, N. (2021). Ai and machine learning techniques in the development of Intelligent Tutoring System: A review. In 2021 International Conference on innovation and Intelligence for informatics, computing, and technologies (3ICT) (pp. 403-410). IEEE. https://doi.org/10.1109/3ICT53449.2021.9582029

Anwar, A., Haq, I. U., Mian, I. A., Shah, F., Alroobaea, R., Hussain, S., ... & Umar, F. (2022). Applying real-time dynamic scaffolding techniques during tutoring sessions using intelligent tutoring systems. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/6006467

Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26, 600-614. https://doi.org/10.1007/s40593-016-0105-0

Bardin, L. (1977). Análise de conteúdo. Lisboa: edições, 70, 225.

Brasil. (2017). Base Nacional Comum Curricular. Educação é a Base. Brasília: Ministério da Educação.

Cohen, L., Manion, L., & Morrison, K. (2000). Research Methods in Education (5th ed.). Routledge. https://doi.org/10.4324/9780203224342

Crutzen, R., & Peters, G. J. Y. (2018). Evolutionary learning processes as the foundation for behaviour change. Health Psychology Review, 12(1), 43-57. https://doi.org/10.1080/17437199.2017.1362569

Cukurova, M., Khan-Galaria, M., Millán, E., & Luckin, R. (2022). A learning analytics approach to monitoring the quality of online one-to-one tutoring. Journal of Learning Analytics, 9(2), 105-120. https://doi.org/10.18608/jla.2022.7411

Da Silva, A. V. M. (2016). A pedagogia tecnicista e a organização do sistema de ensino brasileiro. Revista HISTEDBR On-line, 16(70), 197-209. https://doi.org/10.20396/rho.v16i70.8644737

De Almeida, G. M., Sandro Gomes, A., de Almeida, J. C., Dias Nogueira, T. J. D., Ferreira Lima, R. M., Suruagy de Melo, T., & de Mello, L. A. (2020). Evaluating entrepreneurial perceptions on blended learning. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). https://doi.org/10.1109/SMC42975.2020.9282851

DeCarvalho, R. J. (1991). The humanistic paradigm in education. The Humanistic Psychologist, 19(1), 88-104. https://doi.org/10.1080/08873267.1991.9986754

Derobertmasure, A., & Robertson, J. E. (2014). Data analysis in the context of teacher training: code sequence analysis using QDA Miner®. Quality & Quantity, 48(4), 2255–2276. https://doi.org/10.1007/s11135-013-9890-9

Feng, S., Magana, A. J., & Kao, D. (2021). A systematic review of literature on the effectiveness of intelligent tutoring systems in STEM. 2021. IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/FIE49875.2021.9637240

Gaspar, R. (2023). Experiência e educação. Petropolis. Editora vozes. Traduzido de: Dewey, J. (1971).

Gilbert, S., & Dorneich, M. (2018). Applying Human-Agent Team Concepts to the Design of Intelligent Team Tutoring Systems. In Intelligent Tutoring Systems: 14th International Conference, ITS 2018, Montreal, QC, Canada, June 11–15, 2018, Proceedings (Vol. 10858, p. 457). Springer.

Glaser, B., & Strauss, A. (1999). Discovery of Grounded Theory: Strategies for Qualitative Research (1st ed.). Routledge. https://doi.org/10.4324/9780203793206

Gutierrez, F., & Atkinson, J. (2011). Adaptive feedback selection for intelligent tutoring systems. Expert Systems with Applications, 38(5), 6146–6152. https://doi.org/10.1016/j.eswa.2010.11.058

Han, E. R., & Chung, E. K. (2022). The Relationship Between Social Presence and Learning Satisfaction in Videoconferencing Problem-Based Learning. Korean Medical Education Review, 24(1), 56-62. https://doi.org/10.1145/3572549.3572597

Han, J., Zhao, W., Jiang, Q., Oubibi, M., & Hu, X. (2019). Intelligent tutoring system trends 2006-2018: A literature review. In 2019 eighth international conference on educational innovation through technology (EITT) (pp. 153-159). IEEE. https://doi.org/10.1109/EITT.2019.00037

Hickey, D. T., Harris, T., & Lee, H. (2022). Dimensions of Assessment in Online and Open Education in Terms of Purpose, Function and Theory. In Handbook of Open, Distance and Digital Education. 1-14. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0351-9_80-1#DOI

Horst, H., Hjorth, L., & Tacchi, J. (2012). Rethinking ethnography: An introduction. Media International Australia, 145(1), pp. 86-93.

Howard, S. K., Swist, T., Gasevic, D., Bartimote, K., Knight, S., Gulson, K., Apps, T., Peloche, J., Hutchinson, N., & Selwyn, N. (2022). Educational data journeys: Where are we going, what are we taking and making for AI? Computers and Education: Artificial Intelligence, 3(100073), 100073. https://doi.org/10.1016/j.caeai.2022.100073

Huang, A. (2008, April). Similarity measures for text document clustering. In Proceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand, 4, 9-56.

Jelfs, A., Richardson, J. T. E., & Price, L. (2009). Student and tutor perceptions of effective tutoring in distance education. Distance Education, 30(3), 419–441. https://doi.org/10.1080/01587910903236551

Jiménez, S., Juárez-Ramírez, R., Castillo Topete, V., & Ramírez-Noriega, A. (2017). Affective dialogue ontology for intelligent tutoring systems: Human assessment approach. In Advances in Intelligent Systems and Computing (p. 608–617). Springer International Publishing. https://doi.org/10.1007/978-3-319-48308-5_58

Ji, S., & Yuan, T. (2022). Conversational intelligent tutoring systems for online learning: What do students and tutors say? 2022 IEEE Global Engineering Education Conference (EDUCON). IEEE. 292-298. https://doi.org/10.1109/EDUCON52537.2022.9766567

Katai, Z., & Iclanzan, D. (2022). Impact of instructor on-slide presence in synchronous e-learning. Education and Information Technologies, 1-27. https://doi.org/10.1007/s10639-022-11306-y

Kelkar, S. (2022). Between AI and Learning Science: The Evolution and Commercialization of Intelligent Tutoring Systems. IEEE Annals of the History of Computing, 44(1), 20-30. https://doi.org/10.1109/MAHC.2022.3143816

Kok, G. (2018). A practical guide to effective behavior change: How to apply theory-and evidence-based behavior change methods in an intervention. 16(5). 156-170. https://doi.org/10.31234/osf.io/r78wh

Latham, A. (2022). Conversational Intelligent Tutoring Systems: The State of the Art. Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics, 77-101. https://doi.org/10.1007/978-3-030-79092-9_4

Maldaner, N., Pozzebon, E., & dos Santos, T. N. (2022). Proposta de implementação da Computação Afetiva no Sistema Tutor Inteligente MAZK: conciliando emoções com o processo de aprendizagem. RENOTE, 20(2). https://doi.org/10.22456/1679-1916.129153

Ogunyemi, A. A., Quaicoe, J. S., & Bauters, M. (2022). Indicators for enhancing learners’ engagement in massive open online courses: A systematic review. Computers and Education Open, 100088.

Pereira, A. J., Gomes, A. S., Primo, T. T., Rodrigues, R. L., Júnior, R. P. M., & Moreira, F. (2023). Learning Mediated by Social Network for Education in K-12: Levels of Interaction, Strategies, and Difficulties. Education Sciences, 13(2), 100. https://doi.org/10.3390/educsci13020100

Pham, C. K., Chong, S. L., & Wan, R. (2022, October). Students’ Experience of Social Presence in Online Learning. In Proceedings of the 14th International Conference on Education Technology and Computers. 299-303. https://doi.org/10.1145/3572549.3572597

Piaget, J. (1997). The principles of genetic epistemology: collected works.

Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2015). Digital ethnography: Principles and practice. Sage.

Plotnick, E. (1997). Concept mapping: a graphical system for understanding the relationship between concepts: an ERIC digest. New York: ERIC Clearinghouse on Information and Technology.

Real, L. M. C., & Carvalho, C. J. (2022). Portfólios de aprendizagem: um estudo a partir da presença de estudantes e tutor na educação a distância. Anais do CIET:CIESUD:2022. São Carlos, set. 2022. ISSN 2316-8722. Disponível em: https://cietenped.ufscar.br/submissao/index.php/2022/article/view/2305.

Reis, S. C., Gomes, A. F., & de Souza, R. S. (2014). Explorando a Rede Social Educacional no ensino de línguas: possibilidades, gêneros e multiletramentos. RENOTE, 12(1). https://doi.org/10.22456/1679-1916.50280

Rokhman, N., & Kobar, S. A. (2022). Intelligent Tutoring Systems Authoring Tools For Nonprogrammer Authors: A Systematic Review. SISFORMA, 9(1), 1-8. https://doi.org/10.24167/sisforma.v9i1.3022

Singh, N., Gunjan, V. K., & Chaurasia, M. A. (2022, December). Futuristic Opportunities and Challenges for Cognitive Tutoring Systems. In 2022 5th International Conference on Computational Intelligence and Networks (CINE). 1-6. IEEE. https://doi.org/10.1109/CINE56307.2022.10037263

Srinivasa, K. G., Kurni, M., & Saritha, K. (2022). Harnessing the power of AI to education. In Springer Texts in Education (p. 311–342). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6734-4_13

St-Hilaire, F., Vu, D. D., Frau, A., Burns, N., Faraji, F., Potochny, J., Robert, S., Roussel, A., Zheng, S., Glazier, T., Romano, J. V., Belfer, R., Shayan, M., Smofsky, A., Delarosbil, T., Ahn, S., Eden-Walker, S., Sony, K., Ching, A. O., … Kochmar, E. (2022). A New Era: Intelligent tutoring systems will transform online learning for millions. https://doi.org/10.48550/arXiv.2203.03724

Voogt, J., Fisser, P., Pareja Roblin, N., Tondeur, J., & van Braak, J. (2013). Technological pedagogical content knowledge–a review of the literature. Journal of computer assisted learning, 29(2), 109-121. https://doi.org/10.1111/j.1365-2729.2012.00487.x

Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press.

Yang, C., Chiang, F.-K., Cheng, Q., & Ji, J. (2021). Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems. Journal of Educational Computing Research, 59(6), 1015–1035. https://doi.org/10.1177/0735633120986256

Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S.-M. (2022). Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review. IEEE Access, 1. https://doi.org/10.1109/access.2022.3179356

Downloads

Published

2023-09-23

Issue

Vol. 16 No. 3 (2023)

Section

Article

License

Copyright (c) 2023 Aluisio Pereira, Alex Sandro Gomes, Tiago Thompsen Primo

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The BRAJETS follows the policy for Open Access Journals, provides immediate and free access to its content, following the principle that making scientific knowledge freely available to the public supports a greater global exchange of knowledge and provides more international democratization of knowledge. Therefore, no fees apply, whether for submission, evaluation, publication, viewing or downloading of articles. In this sense, the authors who publish in this journal agree with the following terms: A) The authors retain the copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Attribution License (CC BY), allowing the sharing of the work with recognition of the authorship of the work and initial publication in this journal. B) Authors are authorized to distribute non-exclusively the version of the work published in this journal (eg, publish in the institutional and non-institutional repository, as well as a book chapter), with acknowledgment of authorship and initial publication in this journal. C) Authors are encouraged to publish and distribute their work online (eg, online repositories or on their personal page), as well as to increase the impact and citation of the published work.