Abstract—We developed two versions of a system, called iList, that helps students learn linked lists, an important topic in Computer Science curricula. The two versions of iList differ on the level of feedback they can provide to the students, specifically in the explanation of syntax and execution errors. The system has been fielded in multiple classrooms in two institutions. Our results indicate that iList is effective, is considered interesting and useful by the students, and its performance is getting closer to the performance of human tutors. Moreover, the system is being developed in the context of a study of human tutoring, which is guiding the evolution of iList with empirical evidence of effective tutoring. Index Terms—K.3.1.b. Computer-assisted instruction, K.3.2.b Computer science education, I.2.1.d. Education, H.5.2.e. Evaluation/methodology, Constraint-based modeling, Intelligent tutoring systems 1

Abstract- Intelligent Tutoring System (ITS) is no more a young research area as it has already revolutionized e-Learning, curriculum instructions and workplace training. For the last few decades, the field has seen approach towards many possible directions and as a result, numerous ITSs have been developed to date. However, they are mostly being studied in research environment and only a few have claimed to be successful in real classrooms for large number of students. This is mainly because human learning phenomena are very complex and itself is an ongoing research activity right through the history of mankind. This paper attempts to identify some key learning/instructional aspects that must be addressed while designing a successful tutoring system. In this regard, we have reviewed some of the well-known ITSs and report an analysis of their success in modeling these instructional ingredients.