Abstract
Personalized computer-based tutoring demands learning systems and applications that identify and keep personal characteristics and features for each individual learner. This is achieved by the technology of student modeling. One prevalent technique of student modeling is stereotypes. Furthermore, individuals differ in how they learn. So, the way that helps an individual to learn best is crucial for offering her/him an effective tutoring experience. As a consequence, students’ preferable styles of learning should be incorporated into the student model. However, some researchers have concluded that there are individuals that have a mixture of learning styles. That is the reason for the combination of two different learning style models in the presented approach. Particularly, in this paper we present a stereotype student model that combines the Visual, Auditory, Reading/Writing and Kinesthetic (VARK) learning style model and the Herrmann Brain Dominance Instrument (HBDI). The aim of this article is to further enhance the personalization to students’ needs and preferences by introducing this hybrid instrument and using the technology of stereotypes. The gain from this hybrid learning style approach is that we model two different dimensions of the way that a student prefers to learn: i) the sensory modalities of learning and ii) the way of thinking. In this way, the offered tutoring process can be more tailored to each individual student’s needs, respecting the distinct pace of her/his learning. Our novel approach has been incorporated in an e-learning system and was evaluated by 60 undergraduate students in Greece. The evaluation results show a great acceptance rate of the novel hybrid learning style model by students and underline its pedagogical potential.
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Al-Abri, A., Al-Khanjari, Z., Jamoussi, Y., & Kraiem, N. (2019). User modeling for personalized e-learning based on social collaboration interaction. In proceedings of the 4th free and open source software conference, Oman, 2019, 56-61.
Alkhatib, G., & Al-Humaidi, O. (2018). Innovative virtual teams on demand: HBDI-based paradigm. In 8th international congress of information and communication technology. Xiamen, China, 2018, 139–147. https://doi.org/10.1016/j.procs.2018.04.196.
Azzi, I., Jeghal, A., Radouane, A., Yahyaouy, A., & Tairi, H. (2020). A robust classification to predict learning styles in adaptive E-learning systems. Education and Information Technologies, 25, 437–448. https://doi.org/10.1007/s10639-019-09956-6.
Benchoff, D. E., González, M. P., & Huapaya, C. R. (2018). Personalization of tests for formative self-assessment. IEEE Journal of Latin-American Learning Technologies, 13(2), 70–74. https://doi.org/10.1109/RITA.2018.2831759.
Carmona, C., Castillo, G., & Millán, E. (2008). Designing a dynamic Bayesian network for modeling students’ learning styles. In Proceedings of the 8th IEEE international V conference on advanced learning technologies (ICALT 2008) (pp. 346–350), Santander, Cantabria, Spain. https://doi.org/10.1109/ICALT.2008.116.
Chandramouli, K., Stewart, C., Brailsford, T., & Izquierdo, E. (2008). CAE-L: An Ontology Modelling Cultural Behaviour in Adaptive Education. In 2008 Third IEEE international workshop on semantic media adaptation and personalization, Prague. https://doi.org/10.1109/SMAP.2008.24.
Chen, S., & Zhang, J. (2008). The adaptive learning system based on learning style and cognitive state. In 2008 IEEE international symposium on knowledge acquisition and modeling, Wuhan. https://doi.org/10.1109/KAM.2008.60.
Clark, Q., & Magana, A. (2015). Hybrid learning styles. In 122nd ASEE Annual Conference and Exposition, American Society for Engineering Education, Seattle, USA. https://doi.org/10.18260/p.24205
Falloon, G. (2019). Using simulations to teach young students science concepts: An experiential learning theoretical analysis. Computers and Education, 135, 138–159. https://doi.org/10.1016/j.compedu.2019.03.001.
Fleming, N.D. (1995). I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom. In: Zelmer, A. (Ed.), Annual Conference of the Higher Education and Research Development Society of Australiasia, 308–313.
Glushkova, T. (2008). Adaptive model for user knowledge in the e-learning system. In proceedings of the international conference on computer systems and technologies (CompSysTech’08). https://doi.org/10.1145/1500879.1500964.
Grigoriadou, M., Kornilakis, H., Papanikolaou, K. A., & Magoulas, G. D. (2002). Fuzzy inference for student diagnosis in adaptive educational systems. In I. P. Vlahavas & C. D. Spyropoulos (Eds.), Methods and applications of artificial intelligence. Lecture notes in artificial intelligence, Vol. 2308 (pp. 191–202). Berlin: Springer-Verlag. https://doi.org/10.1007/3-540-46014-4_18.
Grubišić, A., Stankov, S., & Žitko, B. (2013). Stereotype student model for an adaptive e-learning system. Int J Comput Inf Eng, 7(4), 440–447. https://doi.org/10.5281/zenodo.1081417.
Herrmann, N. (1991). The creative brain. Journal of Creative Behavior, 25(4), 275–295. https://doi.org/10.1002/j.2162-6057.1991.tb01140.x.
Hosseini, R., Brusilovsky, P., Yudelson, M., & Hellas, A. (2017). Stereotype modeling for problem-solving performance predictions in MOOCs and traditional courses. In proceedings of the. In 25th conference on user modeling, adaptation and personalization, UMAP ‘17, ACM, New York (pp. 76–84). https://doi.org/10.1145/3079628.3079672.
Jayanthi, M. A., Kumar, R. L., & Hari Priya, K. P. (2019). Impact of learning style and personality traits on students in academics. International Journal of Recent Technology and Engineering, 7(4), 224–227.
Kamitsios, M., Chrysafiadi, K., Virvou, M., & Sakkopoulos, E. (2018). A stereotype user model for an educational game: Overcome the difficulties in game playing and focus on the educational goal. In In 9th IEEE international conference on information, intelligence, systems and applications (IISA), Zakynthos, Greece, 2018 (pp. 1–6). https://doi.org/10.1109/IISA.2018.8633655.
Khamparia, A., & Pandey, B. (2020). Association of learning styles with different e-learning problems: A systematic review and classification. Education and Information Technologies, 25, 1303–1331. https://doi.org/10.1007/s10639-019-10028-y.
Lakshmi, V. S., Kavya, U. K., & Ranganadhan, N. S. (2017). Adaptive E-learning student model for autistic students. Journal of Advanced Research in Dynamical and Control Systems, 9, 135–142.
Lynch, T., & Ghergulescu, I. (2016). An evaluation framework for adaptive and intelligent tutoring systems. In E-learn 2016 (AACE-Association for the Advancement of computing in education). USA, 2016, 1385–1390.
Mejia, C., Florian, B., Vatrapu, R., Bull, S., Gomez, S., & Fabregat, R. (2017). A novel web-based approach for visualization and inspection of reading difficulties on university students. IEEE Trans. Learn. Technol.,10, 53–67. https://doi.org/10.1109/TLT.2016.2626292.
Moser, M., Zumbach, J., & J. (2018). Exploring the development and impact of learning styles: An empirical investigation based on explicit and implicit measures. Computers and Education, 125, 146–157. https://doi.org/10.1016/j.compedu.2018.05.003.
Ortega-Alvarez, J. D., Sanchez, W., & Magana, A. J. (2018). Exploring undergraduate students’ computational modeling abilities and conceptual understanding of electric circuits. IEEE Transactions on Education, 61(3), 204–213. https://doi.org/10.1109/TE.2018.2822245.
Papanikolaou, K., Grigoriadou, M., Knornilakis, H., & Magoulas, G. (2003). Personalizing the interaction in a web-based educational hypermedia system: The case of INSPIRE. User Modeling and User-Adapted Interaction, 13, 213–267. https://doi.org/10.1023/A:1024746731130.
Popescu, E. (2009). Diagnosing students’ learning style in an educational hypermedia system cognitive and emotional processes in web-based education: Integrating human factors and personalization. In advances in web-based learning book series (pp. 187–208). IGI global. https://doi.org/10.4018/978-1-60566-392-0.ch011.
Priyaadharshini, M., & Sundaram, B. V. (2018). Evaluation of higher-order thinking skills using learning style in an undergraduate engineering in flipped classroom. Computer Application in Engineering Education, 26(6), 2237–2254. https://doi.org/10.1002/cae.22035.
Rumeser, D., & Emsley, M. (2017). Learning style and learning method preference in project management education: What happens when things get more complex? In: Proceedings of the 11th European conference on games based learning, ECGBL 2017. Academic conferences and publishing international ltd., 860-865.
Sotiropoulos, D. N., Alepis, E., Kabassi, K., Virvou, M., Tsihrintzis, G. A., & Sakkopoulos, E. (2019). Artificial immune system-based learning style stereotypes. International Journal on Artificial Intelligence Tools, 28(4), 1940008. https://doi.org/10.1142/S0218213019400086.
Surjono, H., & Maltby, J. (2003). Adaptive educational hypermedia based on multiple student characteristics, In proceedings of the 2nd international conference on web-based learning (pp. 442–449). Australia: Melbourne. https://doi.org/10.1007/978-3-540-45200-3_41.
Tadlaoui, M. A., Carvalho, R. N., & Khaldi, M. (2017). The initialization of the learner model combining the Bayesian networks and the stereotypes methods. International Journal of Advanced Computer Research, 7(33), 200–212. https://doi.org/10.19101/IJACR.2017.733024.
Troussas, C., Chrysafiadi, K., & Virvou, M. (2019). An intelligent adaptive fuzzy-based inference system for computer-assisted language learning. Expert Systems with Applications, 127, 85–96. https://doi.org/10.1016/j.eswa.2019.03.003.
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Troussas, C., Chrysafiadi, K. & Virvou, M. Personalized tutoring through a stereotype student model incorporating a hybrid learning style instrument. Educ Inf Technol 26, 2295–2307 (2021). https://doi.org/10.1007/s10639-020-10366-2
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DOI: https://doi.org/10.1007/s10639-020-10366-2