Intelligent Tutor for Python Programming

Abstract

Since IT is an emerging field in modern world, many students try to enter to IT field. All of those students have to learn programming in this context. Teaching programming is a challenging task because it requires a lot of metacognitive skills of the student. The most effective teaching method for programming is individual tutoring. By that teacher can identify individual skill levels of the student and customize teaching methodologies according to them. But individual tutoring requires large amount of resources and impractical with higher number of students. Intelligent Tutoring Systems can be taken as an alternative solution for this. Python is one of the leading object oriented programming language picked by industry specialists and its demand is becoming high day-by-day. Although there are several ITS have been built to teach Python language, there aren’t any web based system. Also they have many shortcomings such as not giving customized feedback to the student. In other hand the students in general doesn’t hit the target of expectation and the volume of enrollments for such courses decrease because of not having independent learning tools for python programming as still the community is not moved to that direction like other popular programming languages. This research addresses it by designing a versatile Intelligent Tutoring System to teach Python Programming with efficient feedback mechanism. When teaching programming, it requires giving practical exercises to the students. The ITS should have the ability to handle all answers and the feedbacks must be prepared differently. This research mainly focused on finding a method on handling feedbacks on programming solutions given by the students by using pedagogical theories. Here the solutions provided by students are checked using and run to find the correctness of the answer. Then the system provides customized feedback for the solutions after analyzing them. The subject content is divided in to several topics and system maintains and stores students’ knowledge level for each topic. Using that information, system guides the student to the next exercise according to their current knowledge level. The system was evaluated by a set of Python teachers and students. The system has been given them for several weeks to use and collected feedback on the characteristics of the system. Also it has been collected comments regarding further improvements of the system. Based on the results of the evaluation it has been concluded that the system has positive attributes to improve students’ knowledge in Python programming.