An adaptive online learning testing system

Abstract

In the context of promoting personalized Internet education, the latest achievements on pedagogy and psychology are applied to the burgeoning computer online learning testing methods in this research. Compared with the traditional way, we propose a novel adaptive selection strategy of examination questions, and adopt the Monte Carlo simulation algorithm to validate the effectiveness of our new method in the following aspects, including item exposure rate, testing library average exposure rate, testing accuracy and testing efficiency, which shows that new strategy achieves better ability distinction from different levels of learners. So we provide a new way for learners to carry out personalized online learning ability testing.