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Personalized Feedback Enhanced by Natural Language Processing in Intelligent Tutoring Systems

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Augmented Intelligence and Intelligent Tutoring Systems (ITS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13891))

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Abstract

This paper proposes a novel approach for enhancing feedback in intelligent tutoring systems (ITSs) for Java programming using natural language processing (NLP). The proposed approach overcomes the limitations of traditional rule-based feedback generation systems and provides more personalized and relevant feedback to learners. The architecture includes three main components: a natural language parser (that takes as input comments and/or questions of the user that can be inserted through a text box in the user interface.), a feedback generator, and a feedback evaluator. The natural language parser is responsible for converting the unstructured text input of the learner into structured data, which can be analyzed for generating feedback. The feedback generator component then processes this data and generates personalized feedback for the learner based on their specific needs. Finally, the feedback evaluator component assesses the quality of the generated feedback and determines its helpfulness to the learner. The evaluation results are promising, indicating that using NLP techniques can improve the overall performance of intelligent tutoring systems and provide a more personalized learning experience for students.

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Authors and Affiliations

  1. Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece

    Christos Troussas, Christos Papakostas, Akrivi Krouska, Phivos Mylonas & Cleo Sgouropoulou

Authors
  1. Christos Troussas
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  2. Christos Papakostas
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  3. Akrivi Krouska
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  4. Phivos Mylonas
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  5. Cleo Sgouropoulou
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Corresponding author

Correspondence to Christos Troussas .

Editor information

Editors and Affiliations

  1. University of Montreal, Montreal, Canada

    Claude Frasson

  2. University of West Attica, Athens, Greece

    Phivos Mylonas

  3. University of West Attica, Athens, Greece

    Christos Troussas

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Troussas, C., Papakostas, C., Krouska, A., Mylonas, P., Sgouropoulou, C. (2023). Personalized Feedback Enhanced by Natural Language Processing in Intelligent Tutoring Systems. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_58

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  • DOI: https://doi.org/10.1007/978-3-031-32883-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32882-4

  • Online ISBN: 978-3-031-32883-1

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