A rule-based expert system for automatic question classification in mathematics adaptive assessment on indonesian elementary school environment

This paper is part of research in developing a competency-based assessment system for mathematics in Indonesian elementary school environment. An essential task is to accurately classify questions based on competency and difficulty level. Thus, an expert system is needed to classify those questions since competency information is often manually defined by experts. The objectives of this work are replacing a human expert’s role in the related knowledge engineering process and providing a rule-based expert system to supersede an expert to classify the questions. Five types of the rule-based algorithm: OneR, RIPPER, PART, FURIA, and J48, were applied to the dataset, which is comprised of 9454 real mathematics examination questions collected from several Indonesian elementary schools. Following the knowledge engineering principles, these algorithms generated the classification rules based on a pattern of the data. The rules of the best performing algorithm were utilized by a knowledge base for inference. Finally, to be able to fully measure the system performance, ten expert teachers were involved in the question classification step. The results confirm that the system meets the stated objectives in classifying the competency and the difficulty level of a question automatically.