Evaluating the learning outcomes of a fuzzy-based Intelligent Tutoring System

In this paper, we present a full evaluation of the learning outcomes of an Intelligent Tutoring Systems, which teaches the logic of computer programming and the programming language 'C'. The adaptation of the evaluated ITS is based on a fuzzy mechanism, which is responsible to identify the learner's current knowledge level and misconceptions. The system takes into consideration the knowledge dependencies that exist among the domain concept of the learning material and applying fuzzy rules decides about the learning material that has to be delivered to the learner and the lesson sequence. The system was used in real conditions by 70 students of a postgraduate program in Informatics of the University of Piraeus in Greece. After the period of system's experimental usage, the following four metrics-characteristics were calculated: (1) the learners' performance, (2) the number of the learners that dropped out from the usage of the system, (3) the number of learners that succeeded to reach the target knowledge by completing, successfully, the lessons of the system, and (4) the mean number of interactions with the system that are needed until the learner reaches the target knowledge. The calculated numbers were compared with the corresponding results after the usage of a similar Intelligent Tutoring System from which the fuzzy mechanism was absent. For ensuring the validity of the evaluation results, t-tests were conducted. The evaluation results show that the presented fuzzy-based Intelligent Tutoring system increases the learners' knowledge level and offers a personalized learning experience that promotes the active participation of students in the learning process and decreases the number of dropouts.