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Q&A Generation for Flashcards Within a Transformer-Based Framework

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Book cover Higher Education Learning Methodologies and Technologies Online (HELMeTO 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1779))

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Abstract

Flashcards are the main tool used in the Spaced Repetition memorization method, yet there are not always available for many topics due to the high effort required to create them. The combination of Transformer-based models with a Recommender System (RS) can enable a dynamic model to auto generate flashcards recommendation for learners and serious game players in order to improve their skills in learning programming. In previous work we introduced an Intelligent Serious Games (ISG) model that combined Deep Knowledge Tracing (DKT) with a Transformer-based Recommender. The ISG aimed at predicting the outcomes of the next missions in gameplay and enabling flashcard recommendations to complete the missions successfully. This research extends previous work by introducing a novel architecture and specifications for a Transformer-based recommender. We introduce a novel Transformer-based framework tailored to three different NLP tasks to dynamically generate flashcards in the form of supporting paragraphs, questions, and answers. We fine-tuned GPT-2, GPT-Neo, BART and T5 models on three new programming skills datasets, and evaluated them using standard metrics that target coherence and semantics. Our findings revealed that the framework is capable of generating coherent flashcards in a fully automated process using a single input string as prompt.

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Authors and Affiliations

  1. Department of Engineering and Architecture, Università di Parma, Parma, Italy

    Baha Thabet & Francesco Zanichelli

  2. Department of Mathematical, Physical and Computer Sciences, Università di Parma, Parma, Italy

    Niccolò Zanichelli

Authors
  1. Baha Thabet
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  2. Niccolò Zanichelli
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  3. Francesco Zanichelli
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Corresponding author

Correspondence to Baha Thabet .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Institute for Educational Technology, Palermo, Italy

    Giovanni Fulantelli

  2. Universidad Internacional de La Rioja, Logroño, Spain

    Daniel Burgos

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. Unitelma Sapienza University, Rome, Italy

    Marta Cimitile

  5. University of Palermo, Palermo, Italy

    Giosuè Lo Bosco

  6. National Research Council of Italy, Institute for Educational Technology, Palermo, Italy

    Davide Taibi

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Thabet, B., Zanichelli, N., Zanichelli, F. (2023). Q&A Generation for Flashcards Within a Transformer-Based Framework. In: Fulantelli, G., Burgos, D., Casalino, G., Cimitile, M., Lo Bosco, G., Taibi, D. (eds) Higher Education Learning Methodologies and Technologies Online. HELMeTO 2022. Communications in Computer and Information Science, vol 1779. Springer, Cham. https://doi.org/10.1007/978-3-031-29800-4_59

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  • DOI: https://doi.org/10.1007/978-3-031-29800-4_59

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  • Print ISBN: 978-3-031-29799-1

  • Online ISBN: 978-3-031-29800-4

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