Towards Personalized Learning: Implementing Computerized Adaptive Testing for Tailored Educational Experiences

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Puttachat Khuntontong, Thanapapas Horsuwan

Abstract

The integration of computerized adaptive testing (CAT) with the two-parameter logistic (2PL) model of item response theory (IRT) marks a significant advancement in personalized educational assessment, as showcased in "KlassBitsCAT." This innovative framework adapts in real-time to the examinee's ability, ensuring a testing experience that is not only tailored to the individual's performance but also grounded in rigorous statistical analysis and educational principles. The incorporation of question difficulty levels, inspired by Bloom's Taxonomy, adds a nuanced layer to the adaptation process, enabling the system to assess a wide range of cognitive skills from basic understanding to complex problem-solving.

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