Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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High-quality test items are essential for educational assessments, particularly within Item Response Theory (IRT). Traditional validation methods rely on resource-intensive pilot testing to estimate item difficulty and discrimination. More recently, Item-Writing Flaw (IWF) rubrics emerged as a domain-general approach for evaluating test items based on textual features. This method offers a scalable, pre-deployment evaluation without requiring student data, but its predictive validity concerning empirical IRT parameters is underexplored. To address this gap, we conducted a study involving 7,126 multiple-choice questions across various STEM subjects (physical science, mathematics, and life/...

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

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Engaging students in creating novel content, also referred to as learnersourcing, is increasingly recognised as an effective approach to promoting higher-order learning, deeply engaging students with course material and developing large repositories of content suitable for personalised learning. Despite these benefits, some common concerns and criticisms are associated with learnersourcing (e.g., the quality of resources created by students, challenges in incentivising engagement and lack of availability of reliable learnersourcing systems), which have limited its adoption. This paper presents a framework that considers the existing learnersourcing literature, the latest insights from the...

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