Learning programming can prove to be a challenging task for students, as it demands some algorithmic thinking. Teachers usually assist their students by providing accompanying hints, which should successfully guide the students without revealing the final solution. It would be pointless to present the correct solution to the students prematurely, since they would not achieve the desired learning effect. Larger groups of students present an even bigger teaching challenge as assisting individual students takes a substantial amount of time. Bloom's 2-sigma problem assumes that individual teaching, where the teacher provides assistance to only one student at a time, is the most effective form of teaching and should therefore be taken into consideration while planning a lesson. Intelligent tutoring system (ITS) based on the automated tutorial, enables learning in a program environment that substitutes teacher support while retaining some of the elements of individual teaching, such as hints and instant feedback.
In my master's thesis, I have focused on automated tutorial in the CodeQ system, which is used in teaching programming using Python and Prolog programming languages at both the Faculty of Computer and Information Science and the Faculty of Chemistry and Chemical Technology, University of Ljubljana. I analysed the typical (semantic) mistakes in the selected assignments in the collection of submitted student programs. These programs were transformed into abstract syntax trees (AST), from which I created the attributes to form decision trees. Decision trees can be used for identifying the reasons for the incorrect functioning of the programs, as well as the frequent or typical mistakes. A collection of typical mistakes can help build a bug library and produce ITS tips to help at solving problems.
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