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A data-driven approach for the identification of misconceptions in step-based tutoring systems

Math errors are an important part of the learning process. For this reason, diagnosing them can help teachers and intelligent learning environments to choose the most appropriate type of assistance for the learner. In particular, the identification of learner misconceptions can be of special importance because they represent a misunderstanding of math concepts. In this context, this paper proposes the use of clustering algorithms to automatically identify algebra misconceptions from learners’ algebra problem-solving steps in an intelligent learning environment. The computing platform is an intelligent tutoring system that assists students when solving linear equations step by step, by giving minimal and error feedback. The results showed that the model was able to identify some misconceptions already known in the literature, which illustrates the appropriateness of our approach. The automatic identification of misconceptions can help in the identification of new conceptual misunderstanding from a large dataset of math problem-solving, besides give valuable information for teachers and intelligent learning environments to adapt their instruction and assistance.