A New Approach for Identifying Learning Styles Based on Computerized Adaptive Testing

Rami, Samia; Hnida, Meriem; Idrissi, Mohammed Khalidi; Bennani, Samir

doi:10.21125/edulearn.2017.1615

Cited by 2 publications

(2 citation statements)

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“…They are more comfortable with situations that require reflection and deep thinking. They are more theoretician than practical [15]. -Understanding: addresses the channel in which learner progresses towards understanding: sequentially or globally.…”

Section: Introductionmentioning

confidence: 99%

“…A good deal of information used by humans to predict cognitive learning style is based on the learners' behavior shown throughout his training, and it has been hypothesized that learners' traces are directly linked to the his preferred cognitive learning style [15]. Learning plat-forms provide a continuous and non-intrusive way of capturing learners' behavior.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Learning Style Detection in e-Learning Environments using Artificial Neural Network

Rami¹,

Bennani²,

Idrissi³

2022

Int. J. Emerg. Technol. Learn.

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COVID-19 pandemic has impacted all aspects of our lives including learning. With the particular growth of e-learning, teaching approaches are being implemented at a distance on online platforms due to this pandemic. In this context, to make student involved throughout the online course, it is recommended to create an efficient platform similar to the traditional learning mode. In this study, we aims to improve learning style detection process by exploring additional such as cognitive traits. In fact, we have proposed novel approach based on Artificial neural network that classify students according to their level of cognitive learning styles in real-time. The proposed automated approach will certainly provide tutors with exhaustive information that helps them in achieving an improved and innovative online learning method. The results obtained are quite interesting and demonstrate the relevance of our solution.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%