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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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