A Machine Learning Approach to Identify Dependencies Among Learning Objects

Carlo De Medio, Fabio Gasparetti, Carla Limongelli, Filippo Sciarrone, Marco Temperini

Abstract

Selecting and sequencing a set of Learning Objects (LOs) to build a course may turn out to be quite a challenging task. In this paper we focus on such an aspect, related to the verification and respect of the relationships of pedagogical dependence existing between two LOs added to a course (meaning that if a given LO has another one as “pre-requisite”, then any sequencing of the LOs in the course will need to have the latter LO taken by the learners before of the former). In our approach the sequencing of LOs in the course can still be managed by the instructor, basing on her/his taste and preferences, yet s/he can also be helped by a set of suggestions, related to the pre-requisite relationships existing among the LOs selected for the course. Such suggestions (such relationships, in effect) can be computed automatically and provide the instructor with significant help and guidance. We show a light-weight formalization of the LO, and how it can be “represented” by a set of WikiPedia Pages (“topics”); then we show how such set of topics, together with a set of relevant hypotheses we previously defined, can help establish the dependence relationship existing between two LOs. In this endeavor we exploit the classification in categories available for the WikiPedia topics, and obtain interesting results for our framework, in terms of precision and recall of the dependence relationships.

References

  1. Allen, M. and Tay, E. (2012). Wikis as individual student learning tools: The limitations of technology. Int. J. Inf. Commun. Technol. Educ., 8(2):61-71.
  2. Biancalana, C., Gasparetti, F., Micarelli, A., and Sansonetti, G. (2013). Social semantic query expansion. ACM Trans. Intell. Syst. Technol., 4(4):60:1-60:43.
  3. Brusilovsky, P. and Vassileva, J. (2003). Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Life-long Learning, 13:75-94.
  4. Capuano, N., Gaeta, M., Orciuoli, F., and Ritrovato, P. (2009). On-demand construction of personalized learning experiences using semantic web and web 2.0 techniques. In Advanced Learning Technologies, 2009. ICALT 2009. Ninth IEEE International Conference on, pages 484-488.
  5. Cebeci, Z. and Tekdal, M. (2006). Using podcasts as audio learning objects. Interdisciplinary Journal of ELearning and Learning Objects, 2(1):47-57.
  6. Cole, M. (2009). Using wiki technology to support student engagement: Lessons from the trenches. Computers & Education, 52(1):141 - 146.
  7. De Marsico, M., Sterbini, A., and Temperini, M. (2013). A framework to support social-collaborative personalized e-learning. In Kurosu, M. (Ed.) HumanComputer Interaction (Part II), 15th Int. Conf. on Human-Computer Interaction, LNCS 8005, pages 351-360. Springer.
  8. Gabrilovich, E. and Markovitch, S. (2009). Wikipediabased semantic interpretation for natural language processing. J. Artif. Int. Res., 34(1):443-498.
  9. Gasparetti, F., Limongelli, C., and Sciarrone, F. (2015a). A content-based approach for supporting teachers in discovering dependency relationships between instructional units in distance learning environments. In Human-Computer Interaction. Theories, Methods, and Tools - 17th International Conference, HCI Los Angeles, CA, U.S.A., August 2-7, 2015. To appear.
  10. Gasparetti, F., Limongelli, C., and Sciarrone, F. (2015b). Exploiting wikipedia for discovering prerequisite relationships among learning objects. In Proc. IEEE Int. Conf. on Information Technology Based Higher Education and Training, ITHET2015.
  11. Gasparetti, F., Limongelli, C., and Sciarrone, F. (2015c). Wiki course builder: a system for retrieving and sequencing didactic materials from wikipedia. In Proc. IEEE Int. Conf. on Information Technology Based Higher Education and Training, ITHET2015.
  12. Gentili, G., Marinilli, M., Micarelli, A., and Sciarrone, F. (2001). Text categorization in an intelligent agent for filtering information on the web. International Journal of Pattern Recognition and Artificial Intelligence, 15(3):527-549.
  13. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1):10-18.
  14. Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F., and Temperini, M. (2016). A recommendation module to help teachers build courses through the moodle learning management system. New Review of Hypermedia and Multimedia, 22(1-2).
  15. Limongelli, C., Miola, A., Sciarrone, F., and Temperini, M. (2012). Supporting teachers to retrieve and select learning objects for personalized courses in the moodle ls environment. In Proc. 14th IEEE Int. Conf. on Advanced Learning Technologies, Workshop SPEL, pages 518-520. IEEE Computer Society.
  16. Limongelli, C., Sciarrone, F., Starace, P., and Temperini, M. (2010). An ontology-driven olap system to help teachers in the analysis of web learning object repositories. Information Systems Management, 27(3):198-206.
  17. Limongelli, C., Sciarrone, F., and Temperini, M. (2015). A social network-based teacher model to support course construction. Computers in Human Behavior, 51:1077-1085.
  18. Milne, D. and Witten, I. H. (2008). An effective, lowcost measure of semantic relatedness obtained from wikipedia links. In Proceeding of AAAI Workshop on Wikipedia and Artificial Intelligence: an Evolving Synergy, pages 25-30. AAAI Press.
  19. Milne, D. and Witten, I. H. (2013). An open-source toolkit for mining wikipedia. Artif. Intell., 194:222-239.
  20. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition.
  21. Parker, K. and Chao, J. (2007). Wiki as a teaching tool. Interdisciplinary Journal of E-Learning and Learning Objects, 3(1):57-72.
  22. Resnik, P. (2011). Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. CoRR, abs/1105.5444.
  23. Revilla Mun˜oz, O., Alpiste Penalba, F., and Fernández Sánchez, J. (2015). The skills, competences, and attitude toward information and communications technology recommender system: an online support program for teachers with personalized recommendations. New Review of Hypermedia and Multimedia. Published online - Article in Press.
  24. Scheines, R., Silver, E., and Goldin, I. (2014). Discovering prerequisite relationships among knowledge components. In Stamper, J., Pardos, Z., Mavrikis, M., and McLaren, B., editors, Proceedings of the 7th International Conference on Educational Data Mining, pages 355-356. European Language Resources Association (ELRA).
  25. Sciarrone, F. (2013). An extension of the q diversity metric for information processing in multiple classifier systems: A field evaluation. International Journal of Wavelets, Multiresolution and Information Processing, 11(6).
  26. Sterbini, A. and Temperini, M. (2009). Collaborative projects and self evaluation within a social reputationbased exercise-sharing system. In Workshop SPEL, for the IEEE/WIC/ACM Int. Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WIIAT'09, Vol. 3, pages 243-246.
  27. Strube, M. and Ponzetto, S. P. (2006). Wikirelate! computing semantic relatedness using wikipedia. In Proceedings of the 21st National Conference on Artificial Intelligence - Volume 2, AAAI'06, pages 1419-1424. AAAI Press.
  28. Stuurman, S., van Eekelen, M., and Heeren, B. (2012). A new method for sustainable development of open educational resources. In Proceedings of Second Computer Science Education Research Conference, CSERC 7812, pages 57-66, New York, NY, USA. ACM.
  29. Sun, Z. and Qiu, X. (2014). Evaluating the use of wikis for efl: A case study of an undergraduate english writing course in china. Int. J. Inf. Technol. Manage., 13(1):3- 14.
  30. Vuong, A., Nixon, T., and Towle, B. (2011). A method for finding prerequisites within a curriculum. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C., and J. Stamper, J., editors, The 4th International Conference on Educational Data Mining (EDM 2011), pages 211-216.
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Paper Citation


in Harvard Style

De Medio C., Gasparetti F., Limongelli C., Sciarrone F. and Temperini M. (2016). A Machine Learning Approach to Identify Dependencies Among Learning Objects . In Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-179-3, pages 345-352. DOI: 10.5220/0005800503450352


in Bibtex Style

@conference{csedu16,
author={Carlo De Medio and Fabio Gasparetti and Carla Limongelli and Filippo Sciarrone and Marco Temperini},
title={A Machine Learning Approach to Identify Dependencies Among Learning Objects},
booktitle={Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2016},
pages={345-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005800503450352},
isbn={978-989-758-179-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - A Machine Learning Approach to Identify Dependencies Among Learning Objects
SN - 978-989-758-179-3
AU - De Medio C.
AU - Gasparetti F.
AU - Limongelli C.
AU - Sciarrone F.
AU - Temperini M.
PY - 2016
SP - 345
EP - 352
DO - 10.5220/0005800503450352