University mathematical logic courses are part of the curricula of many mechanical engineering and sciences programs. These courses provide a set of logic exercises for students. However, for many novice students, it is very difficult to learn. In particular, these students often get stuck and frustrated when attempting to solve logic exercises. One way to assist first-year university students to overcome difficulties in learning to solve logic problems is to use intelligent tutoring systems (itss) for teaching mathematical logic, which can provide students with personalized hints of students solving process in logic exercises. Many itss for teaching mathematical logic that offer logic exercises provide automated feedback on student solutions. The purpose of this paper is twofold: first, it describes hint factory, a method of automatically generating data-driven hints by using previously collected student data. The method is constructed to be as specific as possible, derived on demand, and directed to the students problem-solving goal, to provide the right type of help at the right time. Besides, the hint factory and similar data-driven techniques have been successfully applied to the domain of programming. Second, it contributes to the literature by giving an overview of research in both domains (mathematics and programming). Iaeme publication scopus indexed.

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