Automatically Predict Question Difficulty for Reading Comprehension Exercises

Question difficulty is a critical indicator for educational examination and personalized learning resources recommendation. Its evaluation mainly based on experts' experience, which is both subjective and labor intensive. Recently, many studies pay more attention to using neural network for question difficulty prediction (QDP). Though these methods have improved efficiency of difficulty prediction, they only regarded the difficulty prediction task as a simple classification or prediction task, which ignored the influence of the input text relation on it, such as confusion relation of multiple choice for English reading comprehension items. Therefore, in this paper, we proposed a Convolutional Neural Network with Multi-view attention (MACNN) to extract different relation from multiple-parts text in reading comprehension exercises with multiple-choice for automatically predicting question difficulty. Our experimental results demonstrate the effectiveness of the proposed framework on a real-world dataset. Besides, we give interpretable insights to analyze the effect of three module we designed with attention mechanism by attention weights visualization experiment.