Personalized Course Recommendation using Item Response Theory

• Main Authors: SHIH, HSIN-HSIU, 石昕秀
• Other Authors: Lu, Horng-Shing
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/3k62qk

碩士 === 國立交通大學 === 統計學研究所 === 105 === Personalized learning is an important research topic in education. Many learning systems consider learner’s ability a significant factor in providing personalized learning services. Oftentimes, these systems employ item response theory to describe the relationship between item difficulty and learner’s ability. However, most learning systems are based on basic item response models which don’t consider in the item features. This study proposes a personalized course recommendation based on the extended item response model, one of the nonlinear mixed models, which takes into account the item features. Results show that the nonlinear mixed model improved the model accuracy and provides better recommendation.