Mathematics is one of the most important subjects which are relating to the development of science and technology. Mathematics has been learned since elementary school until university. It is well known that mathematics is considered as the king of sciences. However, most students assume that mathematics is especially difficult to understand and learn. In recent years, it is too hard to imagine the teaching and learning without computers. When properly applied, computer assisted educational technologies can provide effective means for teaching and learning. Intelligent tutoring systems (itss) are computer-assisted teaching and learning environments created using computational models developed in the learning sciences, cognitive sciences, mathematics, computational linguistics, artificial intelligence, and other relevant fields. Nowadays, there is an increasing technological development in itss. This field of research has become interesting to many researchers. One of the core functions of itss is providing feedback in the form of hints to help students solve problems. However, building a tutor that can provide feedback for a wide variety of problems is thus expensive and time-consuming. Besides, it is common that domain models currently take hundreds of hours to create. Data-driven itss can be used to provide personalized next-step hints automatically and at scale, by using previous students solutions. The interaction network representation is used to record and reuse student work as a domain model. In this paper, we present an analysis and discussion of the interaction network based approach to generate hints for problems in itss for learning mathematics. Iaeme publication.
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