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EduBot: A Proof-of-Concept for a High School Motivational Agent

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Intelligent Data Engineering and Automated Learning – IDEAL 2022 (IDEAL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13756))

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Abstract

Motivation appears to plays a role in student dropout. This paper uses a dataset with high school information (756 entries) for creating a model that relates year failures with the other features on the dataset. The resulting model, based on XGBoost, shows that the top-ranked features are related with motivation issues corroborating with the initial observation. In this sense, a digital assistant embedded with a motivation module may aid on improving motivation and on avoiding dropout. In other words, if the predictor detects year failure possibility it start to act on motivating the student. Considering this paper being direct to underage students, this paper stops on the proof-of-concept level considering the actual dialogues and live tests are expected to performed with the support of educational psychologists.

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Notes

  1. 1.

    https://rasa.com/docs/rasa/.

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Acknowledgements

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020.

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Authors and Affiliations

  1. ALGORITMI Centre, University of Minho, Braga, Portugal

    Hugo Faria, Maria Araújo Barbosa, Bruno Veloso, Francisco S. Marcondes, Dalila Durães & Paulo Novais

  2. High School of Caldas das Taipas, Caldas das Taipas, Guimarães, Portugal

    Celso Lima

Authors
  1. Hugo Faria
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  2. Maria Araújo Barbosa
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  3. Bruno Veloso
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  4. Francisco S. Marcondes
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  5. Celso Lima
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  6. Dalila Durães
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  7. Paulo Novais
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Corresponding author

Correspondence to Dalila Durães .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

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Faria, H. et al. (2022). EduBot: A Proof-of-Concept for a High School Motivational Agent. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_22

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  • DOI: https://doi.org/10.1007/978-3-031-21753-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21752-4

  • Online ISBN: 978-3-031-21753-1

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