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Intelligent Techniques in Personalization of Learning in e-Learning Systems

  • Chapter
Computational Intelligence for Technology Enhanced Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 273))

Abstract

This chapter contains an overview of intelligent techniques that can be applied in different stages of e-learning systems to achieve personalization. It describes examples of their application to various e-learning platforms to create profiles of learners and to define learning path. The typical approach to obtain learner’s profile is the usage one of the clustering methods, such as: the simple k-means, Self Organizing Map, hierarchical clustering or fuzzy clustering. Classification methods like: C4.5 or C.5, k-Nearest Neighbor and Naive Bayes are also useful, but they need to define classes and training patterns by an expert. In contrary, clustering is unsupervised learning method and the categories are discovered by the method itself. The recommending system is responsible for proposing individual learning path for each learner. The most popular approach is an application of the Aprori method which searches for association rules. However, it seems that it is rather inefficient method when the number of data to process is huge. Other methods and models that can be useful for knowledge representation are also discussed. Recommending systems are mainly built as a knowledge based. Most of them are implemented as rule based systems. An interesting approach implementing cased based reasoning paradigm to recommend learning path is described as well. The end of the chapter contains a critical discussion of existing solutions and suggests possible research in this field.

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References

  1. Agarwal, R., Deo, A., Das, S.: Intelligent agents in e-learning. SIGSOFT Softw. Eng. Notes 29(2), 1 (2004)

    Article  Google Scholar 

  2. Al Hamad, A., Yaacob, N., Al-Zoubi, A.Y.: Integrating ’Learning Style’ Information into Personalized e-Learning System. IEEE Multidisciplinary Engineering Education Magazine 3(1), 2–6 (2008)

    Google Scholar 

  3. Baylari, A., Montazer, G.A.: Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications 36, 8013–8021 (2009)

    Article  Google Scholar 

  4. Capuano, N., Gaeta, M., Micarelli, A., Sangineto, E.: An Intelligent Web Teacher System for Learning Personalization and Semantic Web Compatibility. In: Proceedings of the Eleventh International PEG Conference, St. Petersburg, Russia (2003)

    Google Scholar 

  5. Castro, F., Vellido, A., Nebot, A., Mugica, F.: Applying Data Mining Techniques to e-Learning Problems. In: Lakhmi, C. (ed.) Evolution of Teaching and Learning Paradigms in Intelligent Environment, pp. 183–221. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Castro, F., Vellido, A., Nebot, A., Minguillon, J.: Detecting a typical student behaviour on an e-learning system. In: I Simposio Nacional de Tecnologas de la Informacin y las Comunicaciones en la Educacin, Granada, pp. 153–160 (2005)

    Google Scholar 

  7. Chang, Y., Kao, W., Chu, C., Chiu, C.: A learning style classification mechanism for e-learning. Computers & Education 53, 273–285 (2009)

    Article  Google Scholar 

  8. Chen, C., Peng, C., Shiue, J.: Ontology-based concept map for planning personalized learning path. In: IEEE Conference on Cybernetics and Intelligent Systems, pp. 1337–1342 (2008)

    Google Scholar 

  9. Chen, G., Liu, C., Ou, K., Liu, B.: Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. Journal of Educational Computing Research 23(3), 305–332 (2000)

    Article  Google Scholar 

  10. Davy, M.: A Review of Active Learning and Co-Training in Text Classification, Computer Science Technical Report TCD-CS-2005-64, Trinity College Dublin (2005)

    Google Scholar 

  11. Dolog, P., Nejdl, W.: Personalization in Elena: How to cope with personalization in distributed eLearning Networks. In: SINN 2003 eProceedings (2003)

    Google Scholar 

  12. Dolog, P., Henze, N., Nejdl, W., Sintek, M.: Personalization in distributed e-learning environments. In: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, pp. 170–179 (2004)

    Google Scholar 

  13. Esposito, F., Licchelli, O., Semeraro, G.: Discovering Student Models in e-learning Systems. Journal of Universal Computer Science 10(1), 47–57 (2004)

    Google Scholar 

  14. Esposito, F., Licchelli, O., Semeraro, G.: Extraction of User Profiles in e-learning Systems. In: Proceedings of I-KNOW’0, Graz, Austria, pp. 238–243 (2003)

    Google Scholar 

  15. Fazlollahtabar, H., Mahdavia, I.: User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach. Educational Research Review, doi:10.1016/j.edurev.2009.02.001

    Google Scholar 

  16. Gomes, P., Antunes, B., Rodrigues, L., Santos, A., Barbeira, J., Carvalho, R.: Using Ontologies for eLearning Personalization. Communication & Cognition 41(1&2) (January 2008)

    Google Scholar 

  17. Gu, Q., Sumner, T.: Support Personalization in Distributed E-Learning Systems through Learner Modeling. In: 2nd Information and Communication Technologies, 2006. ICTTA 2006, vol. 1, pp. 610–615 (2006)

    Google Scholar 

  18. Hamdi-Cherif, A.: Machine Elearning– Learning Agents and UML for Elearning Settings. International Journal of Education and Information Technologies 1(2), 51 (2008)

    Google Scholar 

  19. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)

    Google Scholar 

  20. Huang, M., Huang, H., Chen, M.: Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications 33, 551–564 (2007)

    Article  Google Scholar 

  21. Hwang, G.-J.: A conceptual map model for developing intelligent tutoring systems. Computer&Education 40(3), 217–235 (2003)

    Google Scholar 

  22. Jing, C., Quan, L.: An adaptive personalized e-learning model. In: IEEE International Symposium on IT in Medicine and Education, 2008. ITME 2008, pp. 806–810 (2008)

    Google Scholar 

  23. Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. In: Eighth IEEE International Conference on Advanced Learning Technologies, pp. 241–245 (2008)

    Google Scholar 

  24. Kwasnicka, H., Szul, D., Markowska-Kaczmar, U., Myszkowski, P.: Learning Assistant- personalizing Learning Path in e-Learning Environments. In: 7 th International Conference Computer Information Systems and Industral Managament Applications (CISIM 2008), Ostrava, pp. 308–314 (2008)

    Google Scholar 

  25. Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268 (2007)

    MATH  MathSciNet  Google Scholar 

  26. Lazcorreta, E., Botella, F., Fernaández-Caballero, A.: Towards personalized recommendation by two-step modified Apriori data mining algorithm. Expert Systems with Applications 35, 1422–1429 (2008)

    Article  Google Scholar 

  27. Lee, M., Chen, S., Chrysostomou, K., Liu, X.: Mining students’ behavior in web-based learning programs. Expert Systems with Applications 36, 3459–3464 (2009)

    Article  Google Scholar 

  28. Li, J., Zaiäne, O.: Combining usage, content, and structure data to improve web site recommendation. In: International conference on ecommerce and web technologies, pp. 305–315 (2004)

    Google Scholar 

  29. Lu, F., Li, X., Liu, Q., Yang, Z., Tan, G., He, T.: Research on Personalized E-Learning System Using Fuzzy Set Based Clustering Algorithm. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 587–590. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  30. Markellou, P., Mousourouli, I., Spiros, S., Tsakalidis, A.: Using semantic web mining technologies for personalized e-learning experiences. In: Proceedings of the web-based education, pp. 461–826 (2005)

    Google Scholar 

  31. McAleese, R.: The knowledge arena as an extension to the concept map: Reflection in action. Interactive Learning Environments 6(3), 251–272 (1998)

    Article  Google Scholar 

  32. Mitchell, M.T.: Machine learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  33. Myszkowski, P., Kwasnicka, H., Markowska-Kaczmar, U.: Data Mining Techniques in e-Learning CelGrid System. In: 7th International Conference Computer Information Systems and Industral Managament Applications (CISIM 2008), Ostrava, pp. 315–319 (2008)

    Google Scholar 

  34. Quinlan, J.R.: Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)

    MATH  Google Scholar 

  35. Radenkovic, B., Despotovic, M., Bogdanovic, Z., Barac, B.: Creating Adaptive Environment for e-Learning Courses. JIOS 33(1), 179–189 (2009)

    Google Scholar 

  36. Rigou, M., Sirmakessis, S., Tsakalidis, A.: Integrating Personalization in E-Learning Communities. Journal of Distance Education Technologies 2(3), 47–58 (2004)

    Google Scholar 

  37. Romero, C., Porras, A., Ventura, S., Hervás, C., Zafra, A.: Using sequential pattern mining for links recommendation in adaptive hypermedia educational systems. In: International Conference Current Developments in Technology-Assisted Education, pp. 1015–1020 (2006)

    Google Scholar 

  38. Romero, C., González, P., Ventura, S., del Jesus, M., Herrera, F.: Evolutionary algorithms for subgroup discovery in e-learning, A practical application using Moodle data. Expert Systems with Applications 36, 1632–1644 (2009)

    Article  Google Scholar 

  39. Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33, 135–146 (2007)

    Article  Google Scholar 

  40. Romero, C., Ventura, S., Delgado, J.A., de Bra, P.: Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems. In: Duval, E., Klamma, R., Wolpers, M. (eds.) EC-TEL 2007. LNCS, vol. 4753, pp. 292–306. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  41. Ruiz, M., Díaz, M., Soler, F., Pérez, J.: Adaptation in current e-learning systems. Computer Standards & Interfaces 30, 62–70 (2008)

    Article  Google Scholar 

  42. Sun, C.-T., Chou, C.: Experiencing coral: design and implementation of distant cooperative learning. IEEE Transactions on Education 39(3), 357–366 (1996)

    Article  Google Scholar 

  43. Sun, P., Cheng, H., Lin, T., Wang, F.: A design to promote group learning in e-learning:Experiences from the field. Computers & Education 50, 661–677 (2008)

    Article  Google Scholar 

  44. Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. In: Workshop on artificial intelligence in CSCL. 16th European conference on artificial intelligence, pp. 17–23 (2004)

    Google Scholar 

  45. Tian, F., Zheng, Q., Gong, Z., Du, J., Li, R.: Personalized Learning Strategies in an intelligent e-Learning Environment. In: Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design, pp. 973–978 (2007)

    Google Scholar 

  46. Tsai, C.J., Tseng, S.S., Lin, C.Y.: A Two-Phase Fuzzy Mining and Learning Algorithm for Adaptive Learning Environment. In: Alexandrov, V.N., Dongarra, J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds.) ICCS-ComputSci 2001. LNCS, vol. 2074, pp. 429–438. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  47. Tzouveli, P., Mylonas, P., Kollias, S.: An intelligent e-learning system based on learner profiling and learning resources adaptation. Computers & Education (2007), doi:10.1016/j.compedu.2007.05.005

    Google Scholar 

  48. Tzouveli, P., Mylonas, P., Kollias, S.: SPERO - A Personalized Integrated E-Learning System. In: Conference proceedings of IADIS, pp. 34–22 (2004)

    Google Scholar 

  49. Ueno, M.: Online outlier detection system for learning time data in e-learning and its evaluation. In: International conference on computers and advanced technology in education, pp. 248–253 (2004)

    Google Scholar 

  50. Wallace, M., Akrivas, G., Stamou, G., Kollias, S.: Representation of user preferences and adaptation to context in multimedia content – based retrieval. In: Proceedings of SOFSEM, Milovy, Czech Republic (2002)

    Google Scholar 

  51. Wang, F.-H., Shao, H.-M.: Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert Systems with Applications 27, 365–377 (2004)

    Article  Google Scholar 

  52. Winston, P.H.: Artificial Intelligence, 3rd edn. Addison-Wesley, Reading (1992)

    Google Scholar 

  53. Zaïane, O.R.: Building a Recommender Agent for e-Learning Systems, Computers in Education, 2002. In: Proceedings of International Conference on Computers in Education, vol. 1, pp. 55–59 (2002)

    Google Scholar 

  54. Zeng, Q., Zhao, Z., Liang, Y.: Course ontology-based user’s knowledge requirement acquisition from behaviors within e-learning systems. Computers & Education 53(3), 809–818 (2009)

    Article  Google Scholar 

  55. Zhang, K., Cui, L., Wang, H., Sui, Q.: Improvement of Matrix-based Clustering Method for Grouping Learners in E-Learning. In: Proceedings of the 11th International Conference on Computer Supported Cooperative Work in Design (2007)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Applied Informatics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Urszula Markowska-Kaczmar, Halina Kwasnicka & Mariusz Paradowski

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  1. Urszula Markowska-Kaczmar
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  2. Halina Kwasnicka
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  3. Mariusz Paradowski
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Editor information

Editors and Affiliations

  1. Department of Languages and Informatics Systems, Polytechnic University of Catalonia, Campus Nord, Ed.Omega, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

  2. Department of Computer Sciences Multimedia and Telecommunications, Open University of Catalonia, Rbla. Poblenou. 156, 08018, Barcelona, Spain

    Santi Caballé  & Thanasis Daradoumis  & 

  3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Information Sciences, Open University of Catalonia, Av. Tibidabo 39-43, 08035, Barcelona, Spain

    Angel Alejandro Juan Perez

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Markowska-Kaczmar, U., Kwasnicka, H., Paradowski, M. (2010). Intelligent Techniques in Personalization of Learning in e-Learning Systems. In: Xhafa, F., Caballé, S., Abraham, A., Daradoumis, T., Juan Perez, A.A. (eds) Computational Intelligence for Technology Enhanced Learning. Studies in Computational Intelligence, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11224-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-11224-9_1

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  • Online ISBN: 978-3-642-11224-9

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