8 resources

  • Christopher Ormerod
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    Oct 22nd, 2022
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    journalArticle
    Christopher Ormerod
    Oct 22nd, 2022

    We introduce a regression-based framework to explore the dependence that global features have on score predictions from pretrained transformer-based language models used for Automated Essay Scoring (AES). We demonstrate that neural networks use approximations of rubric-relevant global features to determine a score prediction. By considering linear models on the hidden states, we can approximate global features and measure their importance to score predictions. This study uses DeBERTa models...

  • Christopher Ormerod
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    Feb 23rd, 2022
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    preprint
    Christopher Ormerod
    Feb 23rd, 2022

    We investigate the effectiveness of ensembles of pretrained transformer-based language models on short answer questions using the Kaggle Automated Short Answer Scoring dataset. We fine-tune a collection of popular small, base, and large pretrained transformer-based language models, and train one feature-base model on the dataset with the aim of testing ensembles of these models. We used an early stopping mechanism and hyperparameter optimization in training. We observe that generally that...

  • Kai North, Christopher Ormerod
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    Oct 22nd, 2025
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    conferencePaper
    Kai North, Christopher Ormerod
    Oct 22nd, 2025
  • Christopher Michael Ormerod, Alexander K...
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    Jul 2nd, 2024
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    preprint
    Christopher Michael Ormerod, Alexander K...
    Jul 2nd, 2024

    Current research on generative language models (GLMs) for automated text scoring (ATS) has focused almost exclusively on querying proprietary models via Application Programming Interfaces (APIs). Yet such practices raise issues around transparency and security, and these methods offer little in the way of efficiency or customizability. With the recent proliferation of smaller, open-source models, there is the option to explore GLMs with computers equipped with modest, consumer-grade...

  • Amy Burkhardt, Sherri Woolf, Christopher...
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    Oct 22nd, 2025
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    conferencePaper
    Amy Burkhardt, Sherri Woolf, Christopher...
    Oct 22nd, 2025
  • Joshua Wilson, Corey Palermo, Paul Deane...
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    Oct 22nd, 2025
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    presentation
    Joshua Wilson, Corey Palermo, Paul Deane...
    Oct 22nd, 2025
  • Okan Bulut, Maggie Beiting-Parrish, Jodi...
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    Jun 27th, 2024
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    preprint
    Okan Bulut, Maggie Beiting-Parrish, Jodi...
    Jun 27th, 2024

    The integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding...

  • Okan Bulut, Maggie Beiting-Parrish, Jodi...
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    Oct 22nd, 2024
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    preprint
    Okan Bulut, Maggie Beiting-Parrish, Jodi...
    Oct 22nd, 2024

    The integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding...

Last update from database: 22/10/2025, 22:15 (UTC)
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