2021
DOI: 10.1108/idd-07-2020-0080
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Learning preference: development in smart learning environments

Abstract: Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings… Show more

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Cited by 4 publications
(6 citation statements)
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“…Although this model focuses on the nine meta-levels of learning, it can be used to direct the implementation of appropriate teaching strategies. Future research could examine the feasibility of the 9-layered pyramid model of meta-learning within educational contexts recruiting learners with different characteristics (i.e., age, people with disabilities) with the use of smart technologies [135,136].…”
Section: Discussionmentioning
confidence: 99%
“…Although this model focuses on the nine meta-levels of learning, it can be used to direct the implementation of appropriate teaching strategies. Future research could examine the feasibility of the 9-layered pyramid model of meta-learning within educational contexts recruiting learners with different characteristics (i.e., age, people with disabilities) with the use of smart technologies [135,136].…”
Section: Discussionmentioning
confidence: 99%
“…Social media tools have changed and reconstructed learners' learning styles and learning preferences (Zhang et al, 2021). Different users prefer different types of social media.…”
Section: Literature Review 21 Mobile Learning Resourcesmentioning
confidence: 99%
“…Zhang, L. et al studied the factors influencing learning preferences on data obtained from platforms and found that students who preferred team-based learning learned better than those who learned under the traditional lecture method [13]. Kan, Y. et al used Bayesian Networks to correlate the relevant attributes of the latent variables (BNLVs) that the User Preference Model (UPM) has, and they found that the learning preferences mainly consisted of learners' time preference, input preference, pacing preference, and sequence preference, etc., and these characteristics reflect learners' learning habits and tendencies [14].…”
Section: Literature Reviewmentioning
confidence: 99%