Recommending learning material in Intelligent Tutoring Systems

• Autorzy: Bernacki J.

Identyfikatory

DOI

10.17951/ai.2016.16.1.1

• Języki publikacji: EN

Abstrakty

EN

Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used.

Słowa kluczowe

EN

e-learning; analytics; ahp; Analytic Hierarchy Process

• Wydawca: Wydawnictwo UMCS
• Czasopismo: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
• Rocznik: 2016
• Tom: Vol. 16, no. 1
• Strony: 1--11
• Opis fizyczny: Bibliogr. 26 poz., tab. rys.

Twórcy

autor

Bernacki J.

• Dapartment of Computer Science, Wroclaw University of Technology

Bibliografia

• [1] Bernacki J., Kozierkiewicz-Hetma«ska A. Creating Collaborative Learning Groups in Intelligent Tutoring Systems, Hwang, D. et al. (Eds.): ICCCI 2014, LNAI 8733, 184-193, 2014
• [2] Bitonto P., Roselli T., Rossano V. Recommendation in ELearning Social Networks, Springer, LNCS 7048, 327-332, 2011
• [3] Bouzeghoub A., Carpentier C., Defude B., Duitama F. A model of reusable educational components for the generation of adaptive courses. In The rst international workshop on semantic web for Web-based learning in conjunction with CAISE'03 conference, Klagenfurt, Autriche, 2003
• [4] Chen C-M., Lee H.-M., Chen Y.-H. Personalized e-learning system using Item Response Theory, Computers and Education, Volume 44, Issue 3, 237-255, 2005
• [5] Ciloglugil B., Inceoglu M.M. User Modeling for Adaptive ELearning Systems, Proc. of ICCSA, Springer-Verlag Berlin Heidelberg, LNCS 7335, 550-561, 2012
• [6] Dolog P., Henze N., Nejdl W., Sintek M. Personalization in distributed elearning environments, ACM, 170-179, 2005
• [7] Essalmi F., Jemni Ben Ayeda L., Jemnia M., Kinshukb, Graf S. A fully personalization strategy of E-learning scenarios, Computers in Human Behavior, Volume 26, Issue 4, 581-591, 2010
• [8] Felder R.M., Silverman L.K. Learning and teaching styles in engineering education, Engr. Education, 78(7), 674-681, 2002z
• [9] Ghauth K., Abdullah N. Learning materials recommendation using good learners' ratings and content-based ltering, Association for Educational Communications and Technology, 711-727, Springer, 2010
• [10] Hsieh T.-C., Wang T.-I., Su C.-Y., Lee M.-C. A fuzzy logicbased personalized learning system for supporting adaptive English learning. Educational Technology & Society, 15(1), 273-288, 2012
• [11] Khribi M., Jemni M., Nasraoui O. Automatic Recommendations for E-Learning Personalization Based in Web Usage Mining Techniques and Information Retrieval, Eight IEEE International Conference on Advanced Learning Techniques, 241-245, 2008
• [12] Klasnja-Milicevic A., Vesin B., Ivanovic M., Budimac Z. ELearning personalization based on hybrid recommendation strategy and learning style identication, Computers and Education 56 (2011), 885-899, Elsevier, 2011
• [13] Kozierkiewicz-Hetmańska A., Nguyen N.T. A method for learning scenario determination and modication in intelligent tutoring systems, Applied Mathematics and Computer Science 21(1): 69-82, 2011
• [14] Kozierkiewicz-Hetmańska A., Nguyen N.T. A Method for Scenario Modication in Intelligent E-Learning Systems Using Graph-Based Structure of Knowledge. Advances in Intelligent Information and Database Systems, 169-179, 2010
• [15] Lu J. A Personalized e-Learning Material Recommender System, Proceedings of the 2nd International Conference on Information Technology for Application ICITA, 2004
• [16] Melis E., Andres E., Budenbender J., Frischauf A., Goguadze G., Libbrecht P. ActiveMath: A generic and adaptive webbased learning environment. International Journal of Articial Intelligence in Education, 12(4), 385-407, 2001
• [17] Memletics Accelerated Learning Manual's Overview Chapter, http://www.memletics.com/overview.asp (last access: March, 2015)
• [18] Murray T. MetaLinks: Authoring and aordances for conceptual and narrative ow in adaptive hyperbooks. International Journal of Articial Intelligence in Education, 13, 199-233, 2003
• [19] Mutter S.A., Psotka J. Intelligent Tutoring Systems: Lessons Learned, Lawrence Erlbaum Associates Inc., 14(6), 544-545, 1988
• [20] Papanikolaou K.A., Grigoriadou M., Kornikalis H., Magoulas G. Personalizing the interaction in a Web-based educational hypermedia system: The case of INSPIRE. User modeling and user-adapted interaction, 213-267, Kluwer Academic Publishers, 2003
• [21] Paragon Learning Style Inventory http://web.calstatela.edu/faculty/jshindl/plsi/ (last access: March 10, 2015)
• [22] Saaty T.L. How to make a decision: The analytic hierarchy process, European Journal of Operational Research, Volume 48, Issue 1, 9-26, 1990
• [23] Salehi M., Kamalabadi I.-N., Ghoushchi M. Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative ltering, Springer, Education and Information Technologies, 713-735, 2012
• [24] Valaski J., Malucelli A., Reinehr S. Recommending Learning Materials according to Ontology-based Learning Styles, Proc. of the 7th International Conference on Information Technology and Applications (ICITA 2011), 71-75, 2011
• [25] Weber G., Kuhl H.-C., Weibelzahl S. Developing Adaptive Internet Based Courses with the Authoring System NetCoach, Proc. of third workshop on hypermedia, LNCS 2266, 226-238, 2001
• [26] Yang T.-C., Hwang G.-J., Yang S. J.-H. Development of an Adaptive Learning System with Multiple Perspectives based on Students' Learning Styles and Cognitive Styles, Educational Technology and Society, 16(4), 185-200, 2013

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).