Towards Automatic Building of Learning Pathways

Patrick Siehndel, Ricardo Kawase, Bernardo Pereira Nunes, Eelco Herder

Abstract

Learning material usually has a logical structure, with a beginning and an end, and lectures or sections that build upon one another. However, in informal Web-based learning this may not be the case. In this paper, we present a method for automatically calculating a tentative order in which objects should be learned based on the estimated complexity of their contents. Thus, the proposed method is based on a process that enriches textual objects with links to Wikipedia articles, which are used to calculate a complexity score for each object. We evaluated our method with two different datasets: Wikipedia articles and online learning courses. For Wikipedia data we achieved correlations between the ground truth and the predicted order of up to 0.57 while for subtopics inside the online learning courses we achieved correlations of 0.793.

References

  1. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2):123-140.
  2. Brusilovsky, P. and Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In The adaptive web, pages 3-53. SpringerVerlag.
  3. Champaign, J. and Cohen, R. (2010). A model for content sequencing in intelligent tutoring systems based on the ecological approach and its validation through simulated students. In Guesgen, H. W. and Murray, R. C., editors, FLAIRS Conference. AAAI Press.
  4. Chen, C.-M. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2):787 - 814.
  5. Farrell, R. G., Liburd, S. D., and Thomas, J. C. (2004). Dynamic assembly of learning objects. In Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, WWW Alt. 7804, pages 162-169, New York, NY, USA. ACM.
  6. Friedman, J. H. and (y X)-values, O. K. (1999). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38:367-378.
  7. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. (2009). The WEKA data mining software: an update. Special Interest Group on Knowledge Discovery and Data Mining Explorer Newsletter, 11(1):10-18.
  8. Jih, H. J. (1996). The impact of learners' pathways on learning performance in multimedia computer aided learning. J. Netw. Comput. Appl., 19(4):367-380.
  9. Kamps, J. and Koolen, M. (2009). Is wikipedia link structure different? In Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM 7809, pages 232-241, New York, NY, USA. ACM.
  10. Kickmeier-Rust, M., Augustin, T., and Albert, D. (2011). Personalized storytelling for educational computer games. In Ma, M., Fradinho Oliveira, M., and Madeiras Pereira, J., editors, Serious Games Development and Applications, volume 6944 of Lecture Notes in Computer Science, pages 13-22. Springer Berlin Heidelberg.
  11. Knauf, R., Sakurai, Y., Takada, K., and Tsuruta, S. (2010). Personalizing learning processes by data mining. In Advanced Learning Technologies (ICALT), 2010 IEEE 10th International Conference on, pages 488 -492.
  12. Kontopoulos, E., Vrakas, D., Kokkoras, F., Bassiliades, N., and Vlahavas, I. (2008). An ontology-based planning system for e-course generation. Expert Systems with Applications, 35(1):398-406.
  13. Limongelli, C., Sciarrone, F., Temperini, M., and Vaste, G. (2009). Adaptive learning with the ls-plan system: A field evaluation. Learning Technologies, IEEE Transactions on, 2(3):203 -215.
  14. Milne, D. and Witten, I. H. (2008). Learning to link with wikipedia. In Proceedings of the 17th ACM conference on Information and knowledge management, CIKM 7808, pages 509-518, New York, NY, USA. ACM.
  15. Milne, D. and Witten, I. H. (2012). An open-source toolkit for mining wikipedia. Artificial Intelligence.
  16. Quinlan, J. R. (1992). Learning with continuous classes. In Proceedings of the 5th Australian joint Conference on Artificial Intelligence, volume 92, pages 343-348. Singapore.
  17. Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., and Murthy, K. R. K. (2000). Improvements to the SMO algorithm for SVM regression. Neural Networks, IEEE Transactions on, 11(5):1188-1193.
  18. Ullrich, C. and Melis, E. (2009). Pedagogically founded courseware generation based on htn-planning. Expert Systems with Applications, 36(5):9319 - 9332.
  19. Wang, Y. and Witten, I. H. (1997). Inducing model trees for continuous classes. In Poster Papers of the 9th European Conference on Machine Learning (ECML 97), pages 128-137. Prague, Czech Republic.
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Paper Citation


in Harvard Style

Siehndel P., Kawase R., Pereira Nunes B. and Herder E. (2014). Towards Automatic Building of Learning Pathways . In Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-758-024-6, pages 270-277. DOI: 10.5220/0004837602700277


in Bibtex Style

@conference{webist14,
author={Patrick Siehndel and Ricardo Kawase and Bernardo Pereira Nunes and Eelco Herder},
title={Towards Automatic Building of Learning Pathways},
booktitle={Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2014},
pages={270-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004837602700277},
isbn={978-989-758-024-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - Towards Automatic Building of Learning Pathways
SN - 978-989-758-024-6
AU - Siehndel P.
AU - Kawase R.
AU - Pereira Nunes B.
AU - Herder E.
PY - 2014
SP - 270
EP - 277
DO - 10.5220/0004837602700277