AUTOMATION OF PERSONALIZED FEEDBACK IN THE PROGRAMMING STUDIES COURSES

The use of automatic feedback can significantly increase the success of beginners in programming, especially for those who have to study inside a large group, and the teacher’s time is limited. The article proposes an approach to the creation of automatic feedback based on previous solutions. The approach is to form a solution space of programs – weighted graph. The nodes in the graph are the program code; the edge weight is the number of changes and actions that need to be performed in order to go from one state to another. To reduce the number of unique solutions, the source code is normalized using a number of transformations and the construction of an abstract syntax tree. Feedback is a hint of the next step, which can be generated after a new solution is added to an existing graph and the path leading to a more correct state is identified. Thus, with the help of feedback you can reach the right decision. Using the solution space also allows you to find out which solutions are most common, which errors occur and which ways they can be corrected students prefer. Since this approach is based solely on data, the teacher does not need significant interaction with, which makes it scalable and adaptable.

1. Wolfgang Menzel. «Using constraint-based modelling to describe the solution space of ill-defined problems in logic programming». Advances in Web Based LearningICWL 2007. Springer Berlin Heidelberg, 2008. 367-379.

2. Jin, Wei «Program representation for automatic hint generation for a datadriven novice programming tutor». Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2012.

3. Xu, Songwen, Yam San Chee. «Transformation-based diagnosis of student programs for programming tutoring systems». Software Engineering, 29.4 (2003): 360-384.

4. Jim Reye. «Design of a knowledge base to teach programming» Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2012.

5. Barnes Tiffany and John Stamper. «Toward automatic hint generation for logic proof tutoring using historical student data». Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2008.

6. Alexeev Y.E. Automatation testing student programs / Y.E. Alexeev, A.V. Kurov // Engineering journal: science and innovations. 2013. – No. 6 (18). URL: http://engjournal.ru/catalog/it/hidden/768.html

7. Veretennikov M.V. Automatation testing computer programs in technical disciplines: «Дистанционные educational technologies. Ways of implementation». No. 1 – Tomsk: TUSUR publishing house, 2004 г. – 130 с., с.38-47

8. Laptev V.V. Method of assessing skills and abilities in teaching programming // Bulletin of Astrakhan State Technical University. Series: Management, Computer Engineering, and Computer Science. Scientific Journal, № 2 / 2013. - Astrakhan: ASTU Publishing House, 2013 - 218 p., P. 194-201

9. Latypova V.A. Methods of testing works with a complex result in the conditions of mixed and remote automated training // Internet magazine "SCIENCE" Vol. 7, No. 3 (2015) http://naukovedenie.ru/PDF/170TVN315.pdf (free access).

10. Mikheev I.V. Software implementation of the dynamic testing curriculum module // Bulletin of the Saratov State Technical University. Publisher: Saratov State Technical University named after Gagarin Yu.A. No. 1 (79), 2015, pp.113-117