Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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International large-scale assessments (ILSAs) have used graphical response-based items to measure student ability for decades, but they have yet to implement automated scoring of these responses and instead rely on human scoring alone. To investigate how scores provided by machine algorithms compare to those provided by human raters, we applied convolutional neural networks (CNNs) to classify image-based responses from eight Timss 2019 items. Our results show that the most accurate CNN models classified over 99% of the image responses into the appropriate scoring category for dichotomous items and almost 98% for one trichotomous item. Additionally, during the modeling process, the CNNs...

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