In this study, we work together with the educational platform PaGamO to create a workable E-Learning system. The purpose of this study is to enhance rural kids’ academic performance and support teachers in assessing their students’ progress. In the platform, the problems answered by students are randomly selected from a large item bank that contains more than one million items. And students only respond to a small proportion of items. As a result, the data is sparse, but the sample size is large (more than 100 thousand students). I use the joint maximum likelihood approach (coded in R) proposed by Chen (2019) to estimate the item parameters.