Example-based feedback provision using structured solution spaces

International Journal of Learning Technology

Volumn 9, Issue 3, 2014, Pages 248-280

  Gross, Sebastian ^a   Mokbel, Bassam ^b   Paassen, Benjamin ^b   Hammer, Barbara ^b   Pinkwart, Niels ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

^b BIELEFELD UNIVERSITY   (Germany)

Author keywords

Example based feedback; Intelligent tutoring system; ITS; Machine learning; Prototype based clustering

Indexed keywords

EID: 84949569788     PISSN: 14778386     EISSN: 17418119     Source Type: Journal    
DOI: 10.1504/IJLT.2014.065752     Document Type: Conference Paper

References (62)

1. Ahtiainen, A., Surakka, S. and Rahikainen, M. (2006) 'Plaggie: GNU-licensed source code plagiarism detection engine for Java exercises', in Proceedings of the 6th Baltic Sea Conference on Computing Education Research: Koli Calling, Baltic Sea'06, pp. 141-142, ACM, New York, USA.
2. Aleven, V. and Koedinger, K. (2002) 'An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor', Cognitive Science, Vol. 26, No. 2, pp. 147-179.
3. Aleven, V., Ashley, K.D., Lynch, C. and Pinkwart, N. (Eds.) (2006) Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems (ITS), Jhongli, Taiwan, National Central University.
4. Aleven, V., Ashley, K.D., Lynch, C. and Pinkwart, N. (Eds.) (2007) Proceedings of the Workshop on AIED Applications for Ill-Defined Domains at the 13th International Conference on Artificial Intelligence in Education (AIED), Los Angeles, CA, USA.
5. Aleven, V., Ashley, K.D., Lynch, C. and Pinkwart, N. (Eds.) (2008) Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains at the 9th International Conference on Intelligent Tutoring Systems (ITS), Montreal, Canada.
6. Bailey, M., Lin, K-I. and Sherrell, L. (2012) 'Clustering source code files to predict change propagation during software maintenance', in Proceedings of the 50th Annual Southeast Regional Conference, ACM-SE '12, pp. 106-111, ACM, New York, NY, USA.
7. Brusilovsky, P. and Yudelson, M. (2008) 'From webex to navex: interactive access to annotated program examples', Proceedings of the IEEE, June, Vol. 96, No. 6, pp. 990-999.
8. Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P. and Glaser, R. (1989) 'Self-explanations: how students study and use examples in learning to solve problems', Cognitive Science, Vol. 13, No. 2, pp. 145-182.
9. Cho, K. and Schunn, C.D. (2007) 'Scaffolded writing and rewriting in the discipline: a web-based reciprocal peer review system', Computers & Education, April, Vol. 48, pp. 409-426.
10. Douglas, R. and Sejnowski, T. (2007) Report of the NSF Workshop on Future Challenges for the Science and Engineering of Learning, July.
11. Fournier-Viger, P., Nkambou, R., Nguifo, E.M. and Mayers, A. (2010) 'Its in ill-defined domains: toward hybrid approaches', in ITS 2010, pp. 749-751, Springer.
12. Goodlad, S. and Hirst, B. (1989) Peer Tutoring: A Guide to Learning by Teaching, Kogan Page.
13. Gross, S., Mokbel, B., Hammer, B. and Pinkwart, N. (2013) 'Towards a domain-independent its middleware architecture', in N-S. Chen, R. Huang, Kinshuh, Y. Li and D.G. Sampson (Eds.): Proceedings of the 13th IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 408-409.
14. Gusfield, D. (1997) Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology, Cambridge University Press.
15. Hammer, B. and Hasenfuss, A. (2007) 'Relational neural gas', in J. Hertzberg, M. Beetz and R. Englert (Eds.): KI 2007: Advances in Artificial Intelligence, 30th Annual German Conference on AI, KI 2007, Vol. 4667 of Lecture Notes in Artificial Intelligence, pp. 190-204, Springer, Berlin.
16. Hammer, B. and Jain, B.J. (2004) 'Neural methods for non-standard data', in ESANN, pp. 281-292.
17. Hammer, B. (2002) 'Compositionality in neural systems', in M. Arbib (Ed.): Handbook of Brain Theory and Neural Networks, 2nd ed., pp. 244-248, MIT Press.
18. Hasenfuss, A. and Hammer, B. (2010) 'Topographic mapping of large dissimilarity datasets', Neural Computation, Vol. 22, No. 9, pp. 2229-2284.
19. Jonassen, D.H. (1997) 'Instructional design models for well-structured and ill-structured problem-solving learning outcomes', Educational Technology Research and Development, Vol. 45, No. 1, pp. 65-94.
20. Kohonen, T. (1995) Self-organizing Maps, Springer.
21. Kop, R. (2011) 'The challenges to connectivist learning on open online networks: learning experiences during a massive open online course', The International Review of Research in Open and Distance Learning, Special Issue-Connectivism: Design and Delivery of Social Networked Learning, Vol. 12, No. 3.
22. Kuhn, A., Ducasse, S. and Gírba, T. (2007) 'Semantic clustering: identifying topics in source code', Information and Software Technology, 12th Working Conference on Reverse Engineering, Vol. 49, No. 3, pp. 230-243.
23. Kulik, J.A. and Kulik, C. (1988) 'Timing of feedback and verbal learning', Rev. of Educational Research, Vol. 58, No. 1, pp. 79-97.
24. Li, M., Chen, X., Li, X., Ma, B. and Vitanyi, P. (2004) 'The similarity metric', Information Theory, IEEE Transactions on, December, Vol. 50, No. 12, pp. 3250-3264.
25. Liu, Y., Li, Z., Xiong, H., Gao, X. and Wu, J. (2010) 'Understanding of internal clustering validation measures', in G.I. Webb, B. Liu, C. Zhang, D. Gunopulos and X. Wu (Eds.): ICDM, pp. 911-916, IEEE Computer Society.
26. Loll, F. and Pinkwart, N. (2013) 'Lasad: flexible representations for computer-based collaborative argumentation', International Journal of Human-Computer Studies, Vol. 71, No. 1, pp. 91-109.
27. Lynch, C., Ashley, K.D., Aleven, V. and Pinkwart, N. (2006) 'Defining ill-defined domains: a literature survey', in V. Aleven, K.D. Ashley, C. Lynch and N. Pinkwart (Eds.): Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems (ITS), pp. 1-10, National Central University, Jhongli, Taiwan.
28. Lynch, C., Ashley, K.D., Mitrovic, A., Dimitrova, V., Pinkwart, N. and Aleven, V. (Eds.) (2010a) Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains at the 10th International Conference on Intelligent Tutoring Systems (ITS), Pittsburgh, PA, USA.
29. Lynch, C., Ashley, K.D., Pinkwart, N. and Aleven, V. (2010b) 'Concepts, structures, and goals: redefining ill-definedness', International Journal of Artificial Intelligence in Education, Vol. 19, No. 3, pp. 253-266.
30. Müller, M. (2007) Information Retrieval for Music and Motion, Springer-Verlag New York, Inc., Secaucus, NJ, USA.
31. Melis, E. (2005) 'Choice of feedback strategies', in Cognition and Exploratory Learning in the Digital Age (CELDA 2005), IADIS, Vol. 12.
32. Merrill, D.C., Reiser, B.J., Ranney, M. and Trafton, J.G. (1992) 'Effective tutoring techniques: a comparison of human tutors and intelligent tutoring systems', Journal of the Learning Sciences, Vol. 2, No. 3, pp. 277-305.
33. Mitrovic, A., Mayo, M., Suraweera, P. and Martin, B. (2001) 'Constraint-based tutors: a success story', in Proceedings of the 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 931-940, Springer-Verlag, London, UK.
34. Mitrovic, A., Ohlsson, S. and Barrow, D.K. (2013) 'The effect of positive feedback in a constraint-based intelligent tutoring system', Computers & Education, Vol. 60, No. 1, pp. 264-272.
35. Mory, E.H. (2004) Feedback Research Revisited, pp. 745-783, Association for Educational Communications and Technology.
36. Moschitti, A., Pighin, D. and Basili, R. (2008) 'Tree kernels for semantic role labeling', Comput. Linguist., June, Vol. 34, No. 2, pp. 193-224.
37. Murray, T., Blessing, S. and Ainsworth, S. (Eds.) (2003) Authoring Tools for Advanced Technology Learning Environments, Kluwer Academic Publishers, Dordrecht.
38. Neuhaus, M. and Bunke, H. (2006) 'Edit distance-based kernel functions for structural pattern classification', Pattern Recognition, Vol. 39, No. 10, pp. 1852-1863.
39. Nkambou, R., Fournier-Viger, P. and Nguifo, E.M. (2011) 'Learning task models in ill-defined domain using an hybrid knowledge discovery framework', Know-Based Syst., February, Vol. 24, No. 1, pp. 176-185.
40. Ogan, A., Aleven, V. and Jones, C. (2009) 'Advancing development of intercultural competence through supporting predictions in narrative video', International Journal of Artificial Intelligent in Education, August, Vol. 19, No. 3, pp. 267-288.
41. Ohlson, S. (1996) 'Learning from performance errors', Psychological Review, Vol. 103, pp. 241-262.
42. Pekalska, E. and Duin, R.P. (2005) The Dissimilarity Representation for Pattern Recognition. Foundations and Applications, World Scientific.
43. Piaget, J. (1972) The Psychology of the Child, Basic Books, New Yor, NY.
44. Pople, H.E. (1982) 'Heuristic methods for imposing structure on ill-structured problems: the structuring of medical diagnostics', in P. Szolovits (Ed.): AI in Medicine, pp. 119-190, Westview Press, Boulder (CO).
45. Reitman, WR. (1965) Cognition and Thought: An Information Processing Approach, John Wiley and Sons, New York, NY.
46. Renkl, A., Stark, R., Gruber, H. and Mandl, H. (1998) 'Learning from worked-out examples: the effects of example variability and elicited self-explanations', Contemporary Educational Psychology, Vol. 23, No. 1, pp. 90-108.
47. Roll, I., Aleven, V, McLaren, B.M. and Koedinger, K.R. (2011) 'Improving students' help-seeking skills using metacognitive feedback in an intelligent tutoring system', Learning and Instruction, Vol. 21, No. 2, pp. 267-280.
48. Salton, G. and Buckley, C. (1988) 'Term-weighting approaches in automatic text retrieval', Inf. Process. Manage., August, Vol. 24, No. 5, pp. 513-523.
49. Schneider, P., Biehl, M. and Hammer, B. (2009) 'Adaptive relevance matrices in learning vector quantization', Neural Comput, December, Vol. 21, No. 12, pp. 3532-3561.
50. Simon, H.A. (1973) 'The structure of ill-structured problems', Artificial Intelligence, Vol. 4, No. 3, pp. 181-201.
51. Striewe, M. and Goedicke, M. (2011) 'Using run time traces in automated programming tutoring', in G. Roßling, T.L. Naps and C. Spannagel (Eds.): ITiCSE, pp. 303-307, ACM.
52. Striewe, M. and Goedicke, M. (2013) 'Analyse von programmieraufgaben durch softwareproduktmetriken', in A. Spillner and H. Lichter (Eds.): SEUH, Vol. 956 of CEUR Workshop Proceedings, pp. 59-68, CEUR-WS.org.
53. Topping, K. (1998) 'Peer assessment between students in colleges and universities', Rev. of Educational Research, Vol. 68, No. 3, pp. 249-276.
54. Tsovaltzi, D., Melis, E., McLaren, B., Meyer, A-K., Dietrich, M. and Goguadze, G. (2010) 'Learning from erroneous examples: when and how do students benefit from them?', in M. Wolpers, P. Kirschner, M. Scheffel, S. Lindstaedt and V Dimitrova (Eds.): Sustaining TEL: From Innovation to Learning and Practice, Vol. 6383 of Lecture Notes in Computer Science, pp. 357-373, Springer, Berlin Heidelberg.
55. Ullmann, J.R. (1976) 'An algorithm for subgraph isomorphism', J. ACM, January, Vol. 23, No. 1, pp. 31-42.
56. van der Maaten, L. and Hinton, G. (2008) 'Visualizing data using t-sne', J. of Mach. Learn. Res., November, Vol. 9, pp. 2579-2605.
57. Verleysen, M. and Francois, D. (2005) 'The curse of dimensionality in data mining and time series prediction', in Computational Intelligence and Bioinspired Systems, pp. 758-770, Springer.
58. Voss, J.F. (2006) 'Toulmin's model and the solving of ill-structured problems', in D.H.B. Verheij (Ed.): Arguing on the Toulmin Model: New Essays in Argument Analysis and Evaluation, pp. 303-311, Springer, Berlin.
59. Walker, E., Ogan, A., Aleven, V. and Jones, C. (2008) 'Two approaches for providing adaptive support for discussion in an ill-defined domain', in Proc. of a Workshop at ITS, Montreal Canada, June 23, pp. 1-12.
60. Walker, E., Rummel, N., Walker, S. and Koedinger, K. (2012) 'Noticing relevant feedback improves learning in an intelligent tutoring system for peer tutoring', in S. Cerri, W. Clancey, G. Papadourakis and K. Panourgia (Eds.): Intelligent Tutoring Systems, Volume 7315 of Lecture Notes in Computer Science, pp. 222-232, Springer, Berlin Heidelberg.
61. Wittwer, J. and Renkl, A. (2010) 'How effective are instructional explanations in example-based learning? A meta-analytic review', Educational Psychology Review, Vol. 22, No. 4, pp. 393-409.
62. Zakharov, K., Mitrovic, A. and Ohlsson, S. (2005) 'Feedback micro-engineering in eer-tutor', in Proc. AIED 2005, pp. 718-725, Press.