The importance of cognitive skills in human learning and reasoning has become well established in recent years. This motivates the search for a knowledge representation that supports the development of a computational algorithm that performs, as well as instructs such cognitive skills. Cognitive skills can be broadly divided into two types: specific and generic cognitive skills. Intelligent tutoring systems (ITSs) that develop generic cognitive skills are essential for developing classification, generalization, and comparison processes. However, these current systems are inherently open loop and do not utilize feedback, based on the student's competence level, to adapt the learning process. This is a major drawback when attempting to provide a dynamic learner-focused approach for effective instruction. In this paper, we present EpiList II, an ITS that closes the loop and is hence able to monitor and dynamically assess the student's cognitive-skill competence. The results show a significant improvement in positive- and negative-reasoning skills, with 51% of students (up from an average of 8% of students when using an open-loop ITS) acquiring/developing these skills.
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