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Vol.3 , No. 2, Publication Date: Aug. 18, 2016, Page: 7-15
 for programming, a single programming exercise may produce many alternative solutions from students. It is difficult to build ITSs for programming due to the complexity and variety of possible solutions. In order for the students to learn from the system, it is necessary for them to receive feedback on their solutions to the programming exercises. To provide personalized feedback to students who are solving programming exercises effectively, the ITSs for programming must be able to cover a large space of possible solutions. The goal of this paper is to provide a brief review of the works in the literature related this problem.&picture=http://www.aascit.org/aascit/decorators/img/journal/912.jpg)
| [1] | Hieu Bui, Faculty of Information Technology, Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam. |
In intelligent tutoring systems (ITSs) for programming, a single programming exercise may produce many alternative solutions from students. It is difficult to build ITSs for programming due to the complexity and variety of possible solutions. In order for the students to learn from the system, it is necessary for them to receive feedback on their solutions to the programming exercises. To provide personalized feedback to students who are solving programming exercises effectively, the ITSs for programming must be able to cover a large space of possible solutions. The goal of this paper is to provide a brief review of the works in the literature related this problem.
Keywords
Solution Space, Intelligent Tutoring System, Intelligent Programming Tutor, Programming Tutor, Programming Tutoring System, Programming, Programming Exercises, Learning Programming, Teaching Programming
Reference
| [01] | Ade-Ibijola, A., Ewert, S., & Sanders, I. (2015). Introducing Code Adviser: A DFA-driven Electronic Programming Tutor. In Implementation and Application of Automata (pp. 307-312). Springer International Publishing. |
| [02] | AlShamsi, F., & Elnagar, A. (2009, December). JLearn-DG: Java learning system using dependence graphs. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (pp. 633-637). ACM. |
| [03] | Assaf, D., Escherle, N., Basawapatna, A., Maiello, C., & Repenning, A. Retention of Flow: Evaluating a Computer Science Education Week Activity. |
| [04] | Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education (IJAIED), 13, 159-172. |
| [05] | Chrysafiadi, K., & Virvou, M. (2014, July). KEM Cs: A set of student's characteristics for modeling in adaptive programming tutoring systems. InInformation, Intelligence, Systems and Applications, IISA 2014, The 5th International Conference on (pp. 106-110). IEEE. |
| [06] | Fossati, D., Di Eugenio, B., Ohlsson, S. T. E. L. L. A. N., Brown, C., & Chen, L. (2015). Data driven automatic feedback generation in the iList intelligent tutoring system. Technology, Instruction, Cognition and Learning, 10(1), 5-26. |
| [07] | Glassman, E. L., Scott, J., Singh, R., Guo, P. J., & Miller, R. C. (2015). Over Code: Visualizing variation in student solutions to programming problems at scale. ACM Transactions on Computer-Human Interaction (TOCHI), 22(2), 7. |
| [08] | Glassman, E. L., Singh, R., & Miller, R. C. (2014, March). Feature engineering for clustering student solutions. In Proceedings of the first ACM conference on Learning@ scale conference (pp. 171-172). ACM. |
| [09] | Gómez-Albarrán, M. (2005). The Teaching and Learning of Programming: A Survey of Supporting Software Tools. The Computer Journal, 48(2), 130-144. |
| [10] | Gerdes, A. (2012). Ask-Elle: a Haskell Tutor. PhD thesis, Universiteit Utrecht. |
| [11] | Gross, S., Mokbel, B., Paassen, B., Hammer, B., & Pinkwart, N. (2014). Example-based feedback provision using structured solution spaces. International Journal of Learning Technology 10, 9(3), 248-280. |
| [12] | Gross, S., & Pinkwart, N. (2015, July). Towards an Integrative Learning Environment for Java Programming. In Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on (pp. 24-28). IEEE. |
| [13] | Gulwani, S., Radiček, I., & Zuleger, F. (2014, November). Feedback generation for performance problems in introductory programming assignments. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 41-51). ACM. |
| [14] | Holland, J. (2009). A Constraint-Based ITS for the Java Programming Language (Doctoral dissertation, University of Canterbury). Retrieved from https://www.csse.canterbury.ac.nz/research/reports. |
| [15] | Igwe, K., Pillay, N., Corchado, E., Yun-Huoy, C., & Ma, K. (2013, December). Automatic Programming Using Genetic Programming. In Proceedings of the World Congress on Information and Communication Technologies., Hanoi, Vietnam (pp. 339-344). |
| [16] | Jin, W., Barnes, T., Stamper, J., Eagle, M. J., Johnson, M. W., & Lehmann, L. (2012, January). Program representation for automatic hint generation for a data-driven novice programming tutor. In Intelligent Tutoring Systems (pp. 304-309). Springer Berlin Heidelberg. |
| [17] | Keuning, H. (2014). Strategy-based feedback for imperative programming exercises. PhD thesis, Utrecht University. |
| [18] | Keuning, H., Heeren, B., & Jeuring, J. (2014, November). Strategy-based feedback in a programming tutor. In Proceedings of the Computer Science Education Research Conference (pp. 43-54). ACM. |
| [19] | Lahtinen, E., Ala-Mutka, K., & Järvinen, H. M. (2005, June). A study of the difficulties of novice programmers. In ACM SIGCSE Bulletin (Vol. 37, No. 3, pp. 14-18). ACM. |
| [20] | Lazar, T., & Bratko, I. (2014, January). Data-Driven Program Synthesis for Hint Generation in Programming Tutors. In Intelligent Tutoring Systems (pp. 306-311). Springer International Publishing. |
| [21] | Le, N.-T. (2011). Using weighted constraints to build a tutoring system for logic programming. (PhD thesis), University of Hamburg, Germany. |
| [22] | Le, N. T., Loll, F., & Pinkwart, N. (2013). Operationalizing the Continuum between Well-Defined and Ill-Defined Problems for Educational Technology. Learning Technologies, IEEE Transactions on, 6(3), 258-270. |
| [23] | Lee, M. R., & Chen, T. T. (2015). Digital creativity: Research themes and framework. Computers in Human Behavior, 42, 12-19. |
| [24] | Luxton-Reilly, A., Denny, P., Kirk, D., Tempero, E., & Yu, S. Y. (2013, July). On the differences between correct student solutions. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education (pp. 177-182). ACM. |
| [25] | Maghdid, D., Szybek, P., & Führer, C. (2015). A Study on Variation Technique in Courses on Scientific Computing. Science Journal of Education, 3 (June 2015), 60-67. |
| [26] | Nguyen, A., Piech, C., Huang, J., & Guibas, L. (2014, April). Codewebs: scalable homework search for massive open online programming courses. InProceedings of the 23rd international conference on World wide web (pp. 491-502). ACM. |
| [27] | Paaßen, B., Mokbel, B., & Hammer, B. (2015). Adaptive structure metrics for automated feedback provision in Java programming. In English. In: European Symposium on Artificial Neural Networks (ESANN). Ed. by Michel Verleysen.(accepted/in press). Bruges, Belgium. |
| [28] | Piech, C., Sahami, M., Huang, J., & Guibas, L. (2015, March). Autonomously Generating Hints by Inferring Problem Solving Policies. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 195-204). ACM. |
| [29] | Rivers, K., & Koedinger, K. R. (2012, January). A canonicalizing model for building programming tutors. In Intelligent Tutoring Systems (pp. 591-593). Springer Berlin Heidelberg. |
| [30] | Price, T. W., & Barnes, T. (2015, June). An exploration of data-driven hint generation in an open-ended programming problem. In Workshop on Graph-Based Data Mining held at Educational Data Mining (EDM). |
| [31] | Rivers, K., & Koedinger, K. R. (2015). Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor. International Journal of Artificial Intelligence in Education, 1-28. |
| [32] | Singh, R. (2014). Accessible programming using program synthesis (Doctoral dissertation, Massachusetts Institue of Technology). |
| [33] | Sudol, L. A., Rivers, K., & Harris, T. K. (2012). Calculating Probabilistic Distance to Solution in a Complex Problem Solving Domain. International Educational Data Mining Society. |
| [34] | Truong, N. (2007). A web-based programming environment for novice programmers (Doctoral dissertation, Queensland University of Technology). |
| [35] | Thuné, M., & Eckerdal, A. (2009). Variation theory applied to students’ conceptions of computer programming. European Journal of Engineering Education, 34(4), 339-347. |
| [36] | Weragama, D. S. (2013). Intelligent tutoring system for learning PHP. PhD thesis, Queensland University of Technology. |
