Anticipating tutoring demands based on students’ difficulties in Online Learning

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.

Keywords

Online Learning, Tutoring demands, Students’ difficulties

Document Type

Conference paper

Publisher Version

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

Citation

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 P. Zaphiris, & A. Ioannou (Eds.), Learning and Collaboration Technologies (HCII 2024), (Lecture Notes in Computer Science, vol. 14723, pp. 321-332). Springer. https://doi.org/10.1007/978-3-031-61691-4_21. Repositório Institucional UPT. https://hdl.handle.net/11328/5666

Identifiers

https://hdl.handle.net/11328/5666
978-3-031-61690-7
978-3-031-61691-4

Access Type

Restricted Access

License

Attribution (CC-BY)

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