KNOWPATS: PATTERNS OF DECLARATIVE KNOWLEDGE - Searching Frequent Knowledge Patterns about Object-orientation

Peter Hubwieser, Andreas Mühling

Abstract

In order to better understand the structure of students’ knowledge in computer science, we are trying to identify patterns – in form of frequently occurring subgraphs – in concept maps. Concept maps are an exter-nalization of a person’s declarative knowledge represented as a graph. We propose an algorithm that can be employed to identify frequently occurring subgraphs, based on existing algorithms in that field. We are cur-rently working on a project that will gather concept maps form a large group of freshman in the coming semesters, providing us with extensive material for information mining about the structures of knowledge in CS. We hope to get a better understanding of the relationship between knowledge and competence.

References

  1. Albert, D & Steiner, C. M. 2005, 'Empirical Validation of Concept Maps: Preliminary Methodological Considerations' in Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05), ed IEEE.
  2. Anderson, J. R. 2005, Cognitive psychology and its implications. John R. Anderson, Worth Publishers, New York.
  3. Anderson, L. W. 2009, A taxonomy for learning, teaching, and assessing. A revision of Bloom's taxonomy of educational objectives, Longman, New York.
  4. Armstrong, D. J. 2006, 'The quarks of object-oriented development', Commun. ACM, vol. 49, pp. 123-128.
  5. Cordella, L. P., Foggia, P., Sansone, C. & Vento, M. 2004, 'A (sub)graph isomorphism algorithm for matching large graphs', Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 26, no. 10, pp. 1367-1372.
  6. Dominguez, A. K. (2010). Data Mining for Individualised Hints in e-Learning. In Proceedings of the international conference of educational data mining (EDM 2010). Pittsburgh .
  7. Goldsmith, T. E. & Davenport, D. M. 1990, 'Assessing structural similarity of graphs' in Pathfinder associative networks, Ablex Publishing Corp, pp. 75-87.
  8. Hubwieser, P. & Mühling, A. 2011, 'What Students (should) know about Object Oriented Programming' in ICER'11: Proceedings of the 7th International Computing Education Workshop. August 8-9, Providence, Rhode Island, USA, ed ACM, pp. To appear.
  9. Hubwieser, P., Spohrer, M., Steinert, M. & Voß, S. 2008, Algorithmen, objektorientierte Programmierung, Zustandsmodellierung. Schülerbuch - Jahrgangsstufe 10, Klett, Stuttgart.
  10. Inokuchi, A., Washio, T. & Motoda, H. 2000, 'An AprioriBased Algorithm for Mining Frequent Substructures from Graph Data'. Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000, Lyon, France, September 13-16, 2000, Proceedings, eds DA Zighed, HJ Komorowski & JM Zytkow, Springer, pp. 13-23.
  11. Kinchin, I. M., Hay, D. B. & Adams, A. 2000, 'How a qualitative approach to concept map analysis can be used to aid learning by illustrating patterns of conceptual development', Educational Research, vol. 42, no. 1, pp. 43-57.
  12. Madhyastha, T., & Hunt, E. (2009). Mining diagnostic assessment data for concept similarity. Journal of Educational Data Mining, 1(1), 72-91.
  13. Mayring, P. 2000, Qualitative Content Analysis. Available from: http://qualitative-research.net/fqs/fqs-e/2-00inha lt-e.htm.
  14. McClure, J. R., Sonak, B. & Suen, H. K. 1999, 'Concept map assessment of classroom learning: Reliability, validity, and logistical practicality', Sci Teach, vol. 36, pp. 475\textendash492.
  15. Mons, B., Ashburner, M., Chichester, C., van Mulligen, E., Weeber, M., den Dunnen, J., van Ommen, G., Musen, M., Cockerill, M., Hermjakob, H., Mons, A., Packer, A., Pacheco, R., Lewis, S., Berkeley, A., Melton, W., Barris, N., Wales, J., Meijssen, G., Moeller, E., Roes, P., Borner, K. & Bairoch, A. 2008, 'Calling on a million minds for community annotation in WikiProteins', Genome Biology, vol. 9, no. 5, pp. R89.
  16. Mühling, A., Hubwieser, P. & Brinda, T. 2010, 'Exploring Teachers' Attitudes Towards Object Oriented Modelling and Programming in Secondary Schools'. ICER 7810: Proceedings of the Sixth International Workshop on Computing Education Research, ed ACM, ACM, New York, NY, USA, pp. 59-68.
  17. Norman, D. A. 1983, 'Some observations on mental models' in Mental models: eds D Gentner & AL Stevens, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 7- 14.
  18. Pedroni, M. & Meyer, B. 2010, 'Object-Oriented Modeling of Object-Oriented Concepts' in Teaching fundamental concepts of informatics. 4th International Conference on Informatics in Secondary Schools - Evolution and Perspectives, ISSEP 2010, Zurich, Switzerland, January 13-15, 2010; proceedings, eds J Hromkovic, R Královic & J Vahrenhold, Springer, Berlin, pp. 155- 169.
  19. Romero, C., Romero, J. R., Luna, J. M., & Ventura, S. (2010). Mining rare association rules from e-learning data. In Proceedings of the International Conference of Educational Data Mining (EDM 2010). Pittsburgh (pp. 171-180).
  20. Sanders, K., Boustedt, J., Eckerdal, A., McCartney, R., Moström, J. E., Thomas, L. & Zander, C. (eds.) 2008, Student understanding of object-oriented programming as expressed in concept maps, ACM, New York, NY, USA.
  21. Schank, R. C. & Abelson, R. P. 1977, Scripts, Plans, Goals and Understanding: an Inquiry into Human Knowledge Structures, L. Erlbaum, Hillsdale, NJ.
  22. Shavelson, R. J. & Ruiz-Primo, M. A. 1999, 'On the psychomentrics of assessing science understanding.78 in Assessing science understanding, eds J Mintzes, JH Wandersee & JD Novak, Academic Press, San Diego, pp. 304-341.
  23. Vanides, J., Yin, Y., Tomita, M. & Ruiz-Primo Maria Araceli 2005, 'Using Concept Maps in the Classroom', Vol. 28, No. 8, Summer 2005, Science Scope, vol. 28, no. 8, pp. 27-31.
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Paper Citation


in Harvard Style

Hubwieser P. and Mühling A. (2011). KNOWPATS: PATTERNS OF DECLARATIVE KNOWLEDGE - Searching Frequent Knowledge Patterns about Object-orientation . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 350-356. DOI: 10.5220/0003689203580364


in Bibtex Style

@conference{kdir11,
author={Peter Hubwieser and Andreas Mühling},
title={KNOWPATS: PATTERNS OF DECLARATIVE KNOWLEDGE - Searching Frequent Knowledge Patterns about Object-orientation},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={350-356},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003689203580364},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - KNOWPATS: PATTERNS OF DECLARATIVE KNOWLEDGE - Searching Frequent Knowledge Patterns about Object-orientation
SN - 978-989-8425-79-9
AU - Hubwieser P.
AU - Mühling A.
PY - 2011
SP - 350
EP - 356
DO - 10.5220/0003689203580364