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BiGBERT: Classifying Educational Web Resources for Kindergarten-12\(^{th}\) Grades

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Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12657))

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Abstract

In this paper, we present BiGBERT, a deep learning model that simultaneously examines URLs and snippets from web resources to determine their alignment with children’s educational standards. Preliminary results inferred from ablation studies and comparison with baselines and state-of-the-art counterparts, reveal that leveraging domain knowledge to learn domain-aligned contextual nuances from limited input data leads to improved identification of educational web resources.

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Notes

  1. 1.

    For fine-tuning we use 2,655 text passages from NGCS, CCSS, and ICS along with 2,725 from the Brown corpus [5, 12].

  2. 2.

    Due to Terms of Use for Alexa Top Sites, we are unable to share this dataset.

  3. 3.

    We explored SVM as an additional baseline, which performed similarly to BoW and is excluded for brevity.

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Acknowledgments

Work funded by NSF Award # 1763649. The authors would like to thank Dr. Ion Madrazo Azpiazu for his valuable feedback.

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Authors and Affiliations

  1. Department of Computer Science, Boise State University, Boise, ID, USA

    Garrett Allen, Brody Downs, Aprajita Shukla, Casey Kennington, Jerry Alan Fails & Maria Soledad Pera

  2. Department of Literacy, Language and Culture, Boise State University, Boise, ID, USA

    Katherine Landau Wright

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  1. Garrett Allen
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Correspondence to Garrett Allen .

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Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse, Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

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Allen, G. et al. (2021). BiGBERT: Classifying Educational Web Resources for Kindergarten-12\(^{th}\) Grades. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-72240-1_13

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