SciELO - Scientific Electronic Library Online

 
vol.19 número3Identificación del factor humano en el seguimiento de procesos de software en un medio ambiente universitarioWind Flow Analysis of Twisted Savonius Micro-Turbine Array índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Computación y Sistemas

versão On-line ISSN 2007-9737versão impressa ISSN 1405-5546

Comp. y Sist. vol.19 no.3 Ciudad de México Jul./Set. 2015

 

Artículos

 

Wikipedia-based Learning Path Generation

 

Claudia Pérez Martínez1, Gabriel López Morteo1, Magally Martínez Reyes2, Alexander Gelbukh3

 

1 Universidad Autónoma de Baja California, Instituto de Ingeniería, México. claudia.perez92@uabc.edu.mx, galopez@uabc.edu.mx

2 Universidad Autónoma de Baja California, México. mmreyes@hotmail.com

3 Instituto Politécnico Nacional, Centro de Investigación en Computación, México. www.gelbukh.com

Corresponding author is Claudia Pérez-Martínez.

 

Article received on 10/12/2014.
Accepted 15/05/2015.

 

Abstract

We describe a method for automatic generation of a learning path for education or self-education. As a knowledge base, our method uses the semantic structure view from Wikipedia, leveraging on its broad variety of covered concepts. We evaluate our results by comparing them with the learning paths suggested by a group of teachers. Our algorithm is a useful tool for instructional design process.

Keywords: Learning path, educational resources, Wikipedia, adaptive intelligent web-based educational systems, Spanish language.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

Acknowledgment

The fourth author acknowledges the support of the Instituto Politécnico Nacional via the grants SIP 20152095 and SIP 20152100.

 

References

1. UNESCO, (2005). Towards Knowledge Societies.http://unesdoc.unesco.org/images/0014/001418/141843e.pdf.         [ Links ]

2. Smith, P. & Ragan, T. (1999). Instructional design second edition. Wiley.         [ Links ]

3. Brusilovsky, P & Peylo, C. (2003). Adaptive and Intelligent Web-based Educational Systems. Int. J. Artif. Intell. Ed., Vol. 13, pp. 2-4.         [ Links ]

4. Brusilovsky, P. (1999). Adaptive and Intelligent Technologies for Web-based Education. Künstliche. Intelligenz, Vol. 4, 19-25.         [ Links ]

5. WordNet. (2010). Princeton University. https://wordnet.princeton.edu.         [ Links ]

6. Medelyan, O., Milne, D., Legg, C., & Witten, I. H. (2009). Mining Meaning from Wikipedia. International Journal of Human-Computer Studies, Vol. 67, No. 9, pp. 716-754.         [ Links ]

7. Belloch, C. (2013). Diseño Instruccional. http:// www.uv.es/~bellochc/pedagogia/EVA4.pdf        [ Links ]

8. Chen, C., Lee, H., & Chen, Y. (2005). Personalized e-learning system using Item Response Theory. Comput. Educ. Vol. 44, No. 3, pp. 237-255.         [ Links ]

9. Huang, M., Huang, H., & Chen, M. (2007). Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Syst. Appl. Vol. 33, No. 3, pp. 551-564.         [ Links ]

10. Chen, C. (2008). Intelligent web-based learning system with personalized learning path guidance. Comput. Educ. Vol. 51, No. 2. pp. 787-814.         [ Links ]

11. Chen, C. & Duh, L. (2008). Personalized web-based tutoring system based on fuzzy item response theory. Expert Syst. Appl. Vol. 34, No. 4, pp. 2298-2315.         [ Links ]

12. Fazlollahtabar, H. & Mahdavi, I. (2009). User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach. Educational Research Review. Vol. 4, No. 2, pp. 142-155.         [ Links ]

13. Chen, C., Peng, C., Shiu, J. (2008). Ontology-based concept map for planning personalized learning path. 2008 IEEE Conference on Cybernetics and Intelligent Systems, pp. 1337-1342. doi: 10.1109/ICCIS.2008.4670870.         [ Links ]

14. Katuk, N., Ryu, H. (2010). Finding an optimal learning path in dynamic curriculum sequencing with flow experience. 2010 International Conference on Computer Applications and Industrial Electronics (ICCAIE), pp. 227-232.         [ Links ]

15. Fung, S. T., Tam, V., & Lam, E. Y. (2011). Enhancing learning paths with concept clustering and rule-based optimization. In Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies (ICALT'11). IEEE Computer Society, 249-253.         [ Links ]

16. Torres-Moreno, J.-M., Sierra, G., Peinl, P. (2014). A German Corpus for Similarity Detection Tasks. International Journal of Computational Linguistics and Applications, Vol. 5, No. 2, pp. 9-22.         [ Links ]

17. Sidorov, G., Gelbukh, A., Gómez-Adorno, E., Pinto, D. (2014). Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model. Computación y Sistemas, Vol. 18, No. 3, pp. 491-504.         [ Links ]

18. Huynh, D., Tran, D., Ma, W., Sharma, D. (2014). Semantic Similarity Measure Using Relational and Latent Topic Features. International Journal of Computational Linguistics and Applications, Vol. 5, No. 1, pp. 11-25.         [ Links ]

19. Çelebi, A., Özgür, A. (2013). N-gram Parsing for Jointly Training a Discriminative Constituency Parser. Polibits, Vol. 47, pp. 5-12.         [ Links ]

20. Sidorov, G. (2014). Should syntactic n-grams contain names of syntactic relations? International Journal of Computational Linguistics and Applications, Vol. 4, No. 2, pp. 169-188.         [ Links ]

21. Sidorov, G. (2013). Syntactic dependency based n-grams in rule based automatic English as second language grammar correction. International Journal of Computational Linguistics and Applications, Vol. 5, No. 2, pp. 23-46.         [ Links ]

22. Sidorov, G. (2013). Non-continuous Syntactic N-grams. Polibits, Vol. 48, pp. 69-78.         [ Links ]

23. Das, N., Ghosh, S., Gonçalves, T., Quaresma, P. (2014). Comparison of Different Graph Distance Metrics for Semantic Text Based Classification. Polibits, Vol. 49, pp. 51 -57.         [ Links ]

24. Pakray, P., Poria, S., Bandyopadhyay, S., Gelbukh, A. (2011). Semantic textual entailment recognition using UNL. Polibits, Vol. 43, pp. 23-27.         [ Links ]

25. Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., Howard, N. (2014). Dependency-based semantic parsing for concept-level text analysis. Computational Linguistics and Intelligent Text Processing. Proceedings of the 15th International Conference, CICLing 2014, Nepal, Part I, pp. 113-127.         [ Links ]

26. Cambria, E., Poria, S., Bisio, F., Bajpai, R., Chaturvedi, I. (2015). The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis. Computational Linguistics and Intelligent Text Processing. Proceedings of the 16th International Conference, CICLing 2015, Egypt, Part II, pp. 3-22.         [ Links ]

27. Agarwal, B., Poria, S., Mittal, N., Gelbukh, A., Hussain, A. (2015). Concept-level sentiment analysis with dependency-based semantic parsing: A novel approach. Cognitive Computation, Vol. 7, No. 4, pp. 487-499.         [ Links ]

28. Chikersal, P., Poria, S., Cambria, E., Gelbukh, A., Siong, C. E. (2015). Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning. Computational Linguistics and Intelligent Text Processing. Proceedings of the 16th International Conference, CICLing 2015, Egypt, Part II, pp. 49-65.         [ Links ]

29. Poria, S., Cambria, E., Gelbukh, A. (2015). Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-Level Multimodal Sentiment Analysis. Proceedings of EMNLP 2015, pp. 2539-2544.         [ Links ]

30. Schnitzer, S., Schmidt, S., Rensing, C., Harriehausen-Mühlbauer, B. (2014). Combining Active and Ensemble Learning for Efficient Classification of Web Documents. Polibits, Vol. 49, pp. 39-45.         [ Links ]

31. Cobos, C., Mendoza, M., León, E., Manic, M., Herrera-Viedma, E. (2013). TopicSearch— Personalized Web Clustering Engine Using Semantic Query Expansion, Memetic Algorithms and Intelligent Agents. Polibits, Vol. 47, pp. 31-45.         [ Links ]

32. Alonso-Rorís, V. M., Santos Gago, J. M., Pérez Rodríguez, R., Rivas Costa, C., Gómez Carballa, M. A., Anido Rifón, L. (2014). Information Extraction in Semantic, Highly-Structured, and Semi-Structured Web Sources. Polibits, Vol. 49, pp. 69-75.         [ Links ]

33. Jia, L., Yu, C., Meng, W., Zhang, L. (2013). Facet-Driven Blog Feed Retrieval. International Journal of Computational Linguistics and Applications, Vol. 4, No. 1, pp. 175-194.         [ Links ]

34. Neunerdt, M., Reyer, M., Mathar, R. (2013). A POS Tagger for Social Media Texts Trained on Web Comments. Polibits, Vol. 48, pp. 61-68.         [ Links ]

35. Ordoñez, H., Corrales, J. C., Cobos, C. (2014). MultiSearchBP: Environment for Search and Clustering of Business Process Models. Polibits, Vol. 49, pp. 29-37.         [ Links ]

36. Haralambous, Y., Klyuev, V. (2013). Thematically Reinforced Explicit Semantic Analysis. International Journal of Computational Linguistics and Applications, Vol. 4, No. 1, pp. 79-94.         [ Links ]

37. Melara Abarca, R., Pérez-Martínez, C., Gelbukh, A., López Morteo, G., Martínez Reyes, M., Pérez López, M. (2014). Wikification of Learning Objects using Metadata as an Alternative Context for Disambiguation. Computación y Sistemas, Vol. 18, No. 4, pp. 755-765.         [ Links ]

38. Henrich, V., Hinrichs, E., Vodolazova, T. (2012). An Automatic Method for Creating a Sense-Annotated Corpus Harvested from the Web. International Journal of Computational Linguistics and Applications, Vol. 3, No. 2, pp. 47-62.         [ Links ]

39. Reddy B., K., Kumar, K., Krishna, S., Pingali P, Varma, V. (2010). Linking Named Entities to a Structured Knowledge Base. International Journal of Computational Linguistics and Applications, Vol. 1, No. 1-2, pp. 121-136.         [ Links ]

40. Vor Der Brück, T. (2010). Hypernymy Extraction Using a Semantic Network Representation. International Journal of Computational Linguistics and Applications, Vol. 1, No. 1 -2, pp. 105-119.         [ Links ]

41. Homola, P., Kuboň, V. (2010). Exploiting Charts in the MT Between Related Languages. International Journal of Computational Linguistics and Applications, Vol. 1, No. 1 -2, pp. 185-199.         [ Links ]

42. Witten, I.H. & Milne, D. (2008). An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proceeding of AAAI Workshop on Wikipedia and Artificial Intelligence: an Evolving Synergy, AAAI Press, USA, pp. 25-30.         [ Links ]

43. Zesch, T., Gurevych, I., Mühlhäuse, M. (2014). Java-based Wikipedia API. https://www.ukp.tu-darmstadt.de/ukp-home.         [ Links ]

44. Poria, S., Gelbukh, A., Agarwal, B., Cambria, E., Howard, N. (2013). Common sense knowledge based personality recognition from text. Advances in Soft Computing and Its Applications. Proceedings of the 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Mexico, Part II, pp. 484-496.         [ Links ]

45. Sidorov, G., Kobozeva, I., Zimmerling, A., Chanona-Hernández, L., Kolesnikova, O. (2014). Computational Model of Dialog Based on Rules Applied to a Robotic Mobile Guide. Polibits, Vol. 50, pp. 35-42.         [ Links ]

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons