The focus of this study is positive feedback in one-on-one tutoring, its computational modeling, and its application to the design of more effective Intelligent Tutoring Systems. A data collection of tutoring sessions in the domain of basic Computer Science data structures has been carried out. A methodology based on multiple regression is proposed, and some preliminary results are presented. A prototype Intelligent Tutoring System on linked lists has been developed and deployed in a collegelevel

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