The impacts of task relevance and cognitive load on adults’ decision information search

Liu, Xueping; Ji, Lingling; Peng, Huamao

doi:10.1080/13825585.2020.1712320

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…The behaviourist learning theory has been extensively investigated. However, emotional factors of the learner have been given very little attention (Liu et al 2020 ) in mobile learning, especially when environmental and biological contexts (motivational impacts of the learner or their behaviours) are involved (Cavus and Ibrahim 2009 ; Feldon et al 2019 ). To investigate emotional presence in mobile learning, the aim is to identify any key design elements which can enhance the learning pattern of an individual.…”

Section: Mobile Learningmentioning

confidence: 99%

Cognitive load management in mobile learning systems: principles and theories

Curum

Khedo

2020

J. Comput. Educ.

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With the widespread adoption of mobile technologies, mobile-assisted learning is gaining lots of momentum. This new learning paradigm promotes education across different contexts, which is a key factor that contributes to enhancing learning irrespective of the conditions and location of the learner. Therefore, it creates an authentic learning setting whereby students can make meaningful connections to the real world while learning takes place. Previous research works in the field of mobile learning showed that improper design of learning elements is still present in mobile systems and consequently results in poor dynamic content adaptation. Some attempts to adapt learning contents with appropriate instructional design principles are conducted, but with moderate exploitation of smart technological assets in mobile learning systems and limited pedagogical reflections and cognitive factors. In this paper, a learning efficiency model chart is derived using important learning factors that can be considered to enhance mobile learning experiences. Some popular learning theories are analysed and compared with the proposed learning efficiency model chart. This investigation is considered to significantly reduce complexities that exist in mobile learning platforms and promote an enhanced mobile learning experience.

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Section: Mobile Learningmentioning

confidence: 99%