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Anticipating Tutoring Demands Based on Students’ Difficulties in Online Learning

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Learning and Collaboration Technologies (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14724))

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Abstract

Anticipating the tutoring needs in online learning is essential to provide adequate support to students. Feedback and even silence are valuable clues to reveal the level of engagement. Approaches based on Artificial Intelligence (AI) can process this information and alleviate the workload of human tutors. In this study, Natural Language Processing (NLP) techniques were used to assess the performance of classifying students’ difficulties in an Educational Social Network. Difficulties were classified into categories such as “personal”, “technical”, and “others”. The model's performance allows you to anticipate and direct tutoring.

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Notes

  1. 1.

    https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/tokenizer

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Acknowledgements

This work was supported by the FCT – Fundação para a Ciência e a Tecnologia, I.P. [Project UIDB/05105/2020].

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Authors and Affiliations

  1. Informatics Center, Federal University of Pernambuco, Recife, 50740-600, Brazil

    Aluisio José Pereira, Alex Sandro Gomes & Leandro Marques Queiros

  2. Technological Development Center, Federal University of Pelotas, Pelotas, 83321-270, Brazil

    Tiago Thompsen Primo

  3. REMIT, IJP, Universidade Portucalense, Universidade de Aveiro, Porto, Portugal

    Fernando Moreira

  4. IEETA, Aveiro, Portugal

    Fernando Moreira

Authors
  1. Aluisio José Pereira
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  2. Alex Sandro Gomes
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  3. Tiago Thompsen Primo
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  4. Leandro Marques Queiros
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  5. Fernando Moreira
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Corresponding author

Correspondence to Fernando Moreira .

Editor information

Editors and Affiliations

  1. Dept of Multimedia & Graphic Art, Cyprus University of Technology, Limassol, Cyprus

    Panayiotis Zaphiris

  2. Dept of Multimedia and Graphic Arts, Cyprus University of Technology, Limassol, Cyprus

    Andri Ioannou

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Pereira, A.J., Gomes, A.S., Primo, T.T., Queiros, L.M., Moreira, F. (2024). Anticipating Tutoring Demands Based on Students’ Difficulties in Online Learning. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. HCII 2024. Lecture Notes in Computer Science, vol 14724. Springer, Cham. https://doi.org/10.1007/978-3-031-61691-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-61691-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61690-7

  • Online ISBN: 978-3-031-61691-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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