In authors or contributors

5 resources

  • Yiqiu Zhou, Maciej Pankiewicz, Luc Paque...
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    journalArticle
    Yiqiu Zhou, Maciej Pankiewicz, Luc Paque...

    This study examines how Large Language Model (LLM) feedback generated for compiler errors impacts learners’ persistence in programming tasks within a system for automated assessment of programming assignments. Persistence, the ability to maintain effort in the face of challenges, is crucial for academic success but can sometimes lead to unproductive "wheel spinning" when students struggle without progress. We investigated how additional LLM feedback based on the GPT-4 model, provided for...

  • EdArXiv
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    Dec 19th, 2022
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    report
    EdArXiv
    Dec 19th, 2022

    Predictive analytics methods in education are seeing widespread use and are producing increasingly accurate predictions of students’ outcomes. With the increased use of predictive analytics comes increasing concern about fairness for specific subgroups of the population. One approach that has been proposed to increase fairness is using demographic variables directly in models, as predictors. In this paper we explore issues of fairness in the use of demographic variables as predictors of...

  • Yan Tao, Olga Viberg, Ryan S. Baker
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    Oct 27th, 2023
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    journalArticle
    Yan Tao, Olga Viberg, Ryan S. Baker
    Oct 27th, 2023

    Culture fundamentally shapes people's reasoning, behavior, and communication. Generative artificial intelligence (AI) technologies may cause a shift towards a dominant culture. As people increasingly use AI to expedite and even automate various professional and personal tasks, cultural values embedded in AI models may bias authentic expression. We audit large language models for cultural bias, comparing their responses to nationally representative survey data, and evaluate country-specific...

  • Valdemar Švábenský, Ryan S. Baker, André...
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    Oct 27th, 2023
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    conferencePaper
    Valdemar Švábenský, Ryan S. Baker, André...
    Oct 27th, 2023
  • Xiner Liu, Andrés Zambrano, Ryan Baker
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    Mar 5th, 2025
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    journalArticle
    Xiner Liu, Andrés Zambrano, Ryan Baker
    Mar 5th, 2025

    This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies-Zero-shot, Few-shot, and Few-shot with contextual information-as well as the use of embeddings. We do so in the context of qualitatively coding three distinct educational datasets: Algebra I...

Last update from database: 27/10/2025, 21:15 (UTC)
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