Using Learning Styles for Creating and Personalizing Educational Content in Ubiquitous Learning Environments
DOI:
https://doi.org/10.5753/rbie.2020.28.0.133Keywords:
Educational Content Personalization, Learning Styles, Student Model, FSLSM, Ubiquitous Learning EnvironmentsAbstract
The fact that people behave and learn in a different pace requires individual differences to be properly considered in the teaching/learning process. Among several cognitive theories that could be used for this purpose, a promising one is to explore the use of students' learning styles (LSs), with several research studies indicating that their use has positive impacts on learning outcomes. At the same time, Ubiquitous Learning Environments (ULEs) have the potential to make the multimedia authoring of Learning Objects (LOs) an automated process, resulting on even larger educational content repositories and increasing the need for more adequate presentation strategies to students. This article presents an approach for creating and personalizing LOs through a probabilistic proposal of the Felder and Silverman Learning Styles Model. A prototype of the proposed model was integrated into a ubiquitous educational platform and experimented in real settings. Results indicate the existence of correlations between different types of interactions carried out by students and their respective LSs.
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Abowd, G. D., Atkeson, C. G., Feinstein, A., Hmelo, C., Kooper, R., Long, S., … Tani, M. (1996). Teaching and Learning as Multimedia Authoring: The Classroom 2000 Project. In Proceedings of the 4th ACM International Conference on Multimedia (pp. 187–198). Boston, Massachusetts, USA: ACM Press. doi: 10.1145/244130.244191 [GS Search]
Ahmad, N., Tasir, Z., Kasim, J., & Sahat, H. (2013). Automatic Detection of Learning Styles in Learning Management Systems by Using Literature-based Method. Procedia - Social and Behavioral Sciences, 103, 181–189. doi: 10.1016/j.sbspro.2013.10.324 [GS Search]
Alshammari, M., Anane, R., & Hendley, R. J. (2015). The Impact of Learning Style Adaptivity in Teaching Computer Security. In Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education (pp. 135–140). Vilnius, Lithuania: ACM. doi: 10.1145/2729094.2742614 [GS Search]
Alzain, A., Clark, S., Ireson, G., & Jwaid, A. (2018). Learning Personalization Based on Learning Style instruments. Advances in Science, Technology and Engineering Systems Journal, 3(3), 108–115. doi: 10.25046/aj030315 [GS Search]
An, D., & Carr, M. (2017). Learning styles theory fails to explain learning and achievement: Recommendations for alternative approaches. Personality and Individual Differences, 116, 410–416. doi: 10.1016/j.paid.2017.04.050 [GS Search]
Araújo, R. D. (2017). A Computational Architecture for Learning Objects Authoring and Personalization in Ubiquitous Learning Environments (Doctoral dissertation, Universidade Federal de Uberlândia, Brazil). Retrieved from [Link]. doi: 10.14393/UFU.TE.2018.9 [GS Search]
Araújo, R. D., Brant-Ribeiro, T., Ferreira, H. N. M., Dorça, F. A., & Cattelan, R. G. (2016). Segmentação Colaborativa de Objetos de Aprendizagem Utilizando Bookmarks em Ambientes Educacionais Ubíquos. In Proceedings of the XXVII Brazilian Symposium on Computers in Education (SBIE 2016) (pp. 1205–1214). Uberlândia, MG, Brazil: SBC. doi: 10.5753/cbie.sbie.2016.1205 [GS Search]
Araújo, R. D., Brant-Ribeiro, T., Freitas, R. S. de, Dorça, F. A., & Cattelan, R. G. (2014). Autoria automática de objetos de aprendizagem a partir de captura multimídia e associação a estilos de aprendizagem. In Proceedings of the XXV Brazilian Symposium on Computers in Education (SBIE 2014) (pp. 229–238). Dourados, MS, Brazil: SBC. doi: 10.5753/cbie.sbie.2014.229 [GS Search]
Araújo, R. D., Brant-Ribeiro, T., Mendonça, I. E., Mendes, M. M., Dorça, F. A., & Cattelan, R. G. (2017). Social and Collaborative Interactions for Educational Content Enrichment in ULEs. Educational Technology & Society, 20(3). [GS Search]
Barbosa, D. N. F., Barbosa, J. L. V., Bassani, P. B. S., Rosa, J., Martins, M., & Nino, C. (2013). Content Management in a Ubiquitous Learning Environment. International Journal of Computer Applications in Technology, 46(1), 24–35. doi: 10.1504/IJCAT.2013.051385 [GS Search]
Bonate, P. L. (2000). Analysis of Pretest-Posttest Designs (1st ed.). Chapman and Hall/CRC. [GS Search]
Boticario, J. G., Rodriguez-Ascaso, A., Santos, O. C., Raffenne, E., Montandon, L., Roldán, D., & Buendía, F. (2012). Accessible Lifelong Learning at Higher Education: Outcomes and Lessons Learned at two Different Pilot Sites in the EU4ALL Project. Journal of Universal Computer Science, 18(1), 62–85. doi: 10.3217/jucs-018-01-0062 [GS Search]
Brusilovsky, P. (2001). Adaptive Hypermedia. User Modeling and User Adapted Interaction, 11(1), 87–110. doi: 10.1023/A:1011143116306 [GS Search]
Clemente, J., Ramírez, J., & de Antonio, A. (2011). A Proposal for Student Modeling Based on Ontologies and Diagnosis Rules. Expert Systems with Applications, 38(7), 8066–8078. doi: 10.1016/j.eswa.2010.12.146 [GS Search]
CLEO. (2003). CLEO Extensions to the IEEE Learning Object Metadata (Tech. Rep.). Redmond, WA: CLEO Collaborative Partners (Cisco Systems, Inc., IBM Corporation, Microsoft Corporation, Thomson NETg). Retrieved from [Link]
Davis, J. A. (1971). Elementary Survey Analysis. Englewood Cliffs, NJ: Prentice-Hall. [GS Search]
Dorça, F. A., Araújo, R. D., Carvalho, V. C., Resende, D. T., & Cattelan, R. G. (2016). An Automatic and Dynamic Approach for Personalized Recommendation of Learning Objects Considering Students Learning Styles: An Experimental Analysis. Informatics in Education, 15(1), 45–62. doi: 10.15388/infedu.2016.03 [GS Search]
Dorça, F. A., Lima, L. V, Fernandes, M. A., & Lopes, C. R. (2013). Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis. Expert Systems with Applications, 40(6), 2092–2101. doi: 10.1016/j.eswa.2012.10.014 [GS Search]
El-Bishouty, M. M., Chang, T.-W., Graf, S., Kinshuk, & Chen, N.-S. (2014). Smart e-course recommender based on learning styles. Journal of Computers in Education, 1(1), 99–111. doi: 10.1007/s40692-014-0003-0 [GS Search]
Essalmi, F., Ayed, L. J. Ben, Jemni, M., Graf, S., & Kinshuk. (2015). Generalized metrics for the analysis of E-learning personalization strategies. Computers in Human Behavior, 48, 310–322. doi: 10.1016/j.chb.2014.12.050 [GS Search]
Faria, A. R., Almeida, A., Martins, C., Gonçalves, R., & Figueiredo, L. (2015). Personality traits, Learning Preferences and Emotions. In Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering (pp. 63–69). ACM. doi: 10.1145/2790798.2790809 [GS Search]
Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Journal of Engineering Education, 78(7), 674–681. [GS Search]
Felder, R. M., & Soloman, B. A. (n.d.). Index of Learning Styles. Retrieved August 20, 2012, from [Link]
Felder, R. M., & Spurlin, J. (2005). Applications, Reliability and Validity of the Index of Learning Styles. International Journal of Engineering Education, 21(1), 103–112. [GS Search]
Ferreira, H. N. M., Brant-Ribeiro, T., Araújo, R. D., Dorça, F. A., & Cattelan, R. G. (2016). An Automatic and Dynamic Student Modeling Approach for Adaptive and Intelligent Educational Systems Using Ontologies and Bayesian Networks. In Proceedings of the IEEE 28th International Conference on Tools with Artificial Intelligence (pp. 738–745). doi: 10.1109/ICTAI.2016.0116 [GS Search]
Graf, S., Chang, T.-W., Kersebaum, A., Rath, T., & Kurcz, J. (2014). Investigating the Effectiveness of an Advanced Adaptive Mechanism for Considering Learning Styles in Learning Management Systems. In Proceedings of the 14th IEEE International Conference on Advanced Learning Technologies (pp. 112–116). doi: 10.1109/ICALT.2014.41 [GS Search]
Greiff, S., Niepel, C., Scherer, R., & Martin, R. (2016). Understanding students’ performance in a computer-based assessment of complex problem solving: An analysis of behavioral data from computer-generated log files. Computers in Human Behavior, 61, 36–46. doi: 10.1016/j.chb.2016.02.095 [GS Search]
IEEE. (2002). LTSC 1484.12.1, Draft Standard for Learning Object Metadata. Learning Technology Standards Committee of the IEEE. Retrieved from [Link]
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171. doi: 10.1016/j.compedu.2016.12.006 [GS Search]
Li, N., Cohen, W. W., Koedinger, K. R., & Matsuda, N. (2011). A Machine Learning Approach for Automatic Student Model Discovery. In Proceedings of the 4th International Conference on Educational Data Mining (pp. 31–40). IEDMS. [GS Search]
Mahnane, L., & Hafidi, M. (2015). Development and Testing of New E-Learning Hypermedia System. International Journal of Information Technology and Web Engineering, 10(2), 1–15. doi: 10.4018/IJITWE.2015040101 [GS Search]
Mahnane, L., Laskri, M. T., & Trigano, P. (2013). A Model of Adaptive e-learning Hypermedia System based on Thinking and Learning Styles. International Journal of Multimedia and Ubiquitous Engineering, 8(3), 339–350. [GS Search]
Monteiro, B. de S., Oliveira, E., Gomes, A. S., & Mendes Neto, F. M. (2017). Youubi: Ubiquitous Learning Environment. Brazilian Journal of Computers in Education, 25(01), 94–113. doi: 10.5753/rbie.2017.25.01.94 [GS Search]
Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., & Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, 703–714. doi: 10.1016/j.chb.2017.03.028 [GS Search]
Onah, D. F. O., & Sinclair, J. E. (2015). Massive Open Online Courses – An Adaptive Learning Framework. In Proceedings of the 9th International Conference on Technology, Education and Development (pp. 1258–1266). Madrid, Spain: IATED. [GS Search]
Pimentel, M. da G., Ishiguro, Y., Kerimbaev, B., Abowd, G. D., & Guzdial, M. (2001). Supporting Educational Activities through Dynamic Web Interfaces. Interacting with Computers, Special Issue on Interacting with the Active Web, 13(3), 353–374. doi: 10.1016/S0953-5438(00)00042-4 [GS Search]
Santos, O. C., Boticario, J. G., Raffene, E., & Pastor, R. (2007). Why using dotLRN? UNED use cases. In Proceedings of the FLOSS (Free/Libre/Open Source Systems) International Conference 2007 (pp. 195–212). Jerez de la Frontera, Spain: Universidad de Cadiz. [GS Search]
Self, J. A. (1990). Bypassing the intractable problem of student modelling. Intelligent tutoring systems: At the crossroads of artificial intelligence and education, 41, 1–26. [GS Search]
Sottilare, R. A., Brawner, K. W., Sinatra, A. M., & Johnston, J. H. (2017). An Updated Concept for a Generalized Intelligent Framework for Tutoring (GIFT) (Tech. Rep.). Orlando, USA: U.S. Army Research Laboratory - Human Research & Engineering Directorate (ARL-HRED). [GS Search]
Thomson, D., & Mitrovic, A. (2009). Towards a negotiable student model for constraint-based ITSs. In Proceedings of the 17th International Conference on Computers in Education (pp. 83–90). Hong Kong: Asia-Pacific Society for Computers in Education. [GS Search]
Van Assche, F., Campbell, L. M., Rifón, L. A., & Willem, M. (2003). Semantic interoperability: use of vocabularies with learning object metadata. In Proceedings of the 3rd IEEE International Conference on Advanced Learning Technologies (pp. 511–514). IEEE. doi: 10.1109/ICALT.2003.1215220 [GS Search]
Vidal, I. M. G., Costa, E. de B., Silva, L. D. da, Araújo, F. F. de, & Ferreira, R. (2016). A Hypermedia-based Adaptive Educational System for Assisting Students in Systems and Information Technology Domain for Accountability. In Á. Rocha, A. M. Correia, H. Adeli, L. P. Reis, & M. M. Teixeira (Eds.), New Advances in Information Systems and Technologies, Volume 2 (pp. 277–286). Springer, Cham. doi: 10.1007/978-3-319-31307-8_28 [GS Search]
Weiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 94–104. [GS Search]
Wiley, D. A. (2000). Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. The Instructional Use of Learning Objects: Online Version. Bloomington, Indiana, USA: The Agency for Instructional Technology. Retrieved from https://reusability.org/read/ [GS Search]
Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2013). Development of an Adaptive Learning System with Multiple Perspectives based on Students’ Learning Styles and Cognitive Styles. Educational Technology & Society, 16(4), 185–200. [GS Search]
Zhao, X., & Okamoto, T. (2011). Adaptive multimedia content delivery for context-aware u-learning. International Journal of Mobile Learning and Organisation, 5(1), 46–63. doi: 10.1504/IJMLO.2011.038691 [GS Search]
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Copyright (c) 2020 Rafael D. Araújo, Taffarel Brant-Ribeiro, Hiran N. M. Ferreira, Fabiano A. Dorça, Renan G. Cattelan
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