Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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Generating personalized learning paths (PLP) tailored to each student's characteristics and abilities is challenging, especially when using multiple-choice test scores to predict knowledge levels, which can be affected by guessing. We combined the ant colony algorithm (ACO) with Item Response Theory (IRT) to address this issue. This method models the relationship between student responses and their latent abilities, to improve the prediction of knowledge levels resulting in more accurate personalized learning paths (PLP). The parameters used to create the PLP include the initial module, targeted module, knowledge level, pretest score, and difficulty level. The difficulty level is required...

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