Evaluation on text categorization for mathematics application questions

In learning environments, developing intelligent systems that can properly respond learners' emotions is a critial issue for improving learning outcome. For example, systems should consider to replace the current question with an easier one when detecting negative emotions expressed by learners. Conversely, systems can try to retrieve a more challenging question when learners have contempt emotion or feel bored. This paper proposes the use of text categorization to automatically classify mathematics application questions into different difficulty levels. Applications can then benefit from such classification results to develop retrieval systems for proposing questions based on learners' emotion states. Experimental results show that the machine learning algorithm C4.5 achieved the highest accuracy 78.53% in a binary classification task.