Learning Style Recognition: A Neural Network Approach

Rasheed, Fareeha; Wahid, Abdul

doi:10.1007/978-981-13-1580-0_29

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…The 100 students graduated in Computer Science and enrolled in a post-graduate program in Computer Science and Project Management. The 26 descriptors were based on the Sheeba and Krishnan [9] and Bernad et al [5] models. These descriptors were grouped into nine learning objects which are presented to the student in an LMS course.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, the understanding dimension decides whether students incline towards the sequential or global methodology on understanding subjects of study. Sequential students prefer to move toward study and information in a sequential manner, similar to a road map, whereas global students prefer to get an overview and afterward dive into details, attempting to comprehend specific points and link that information with others [9].…”

Section: Learning Styles Model Selectionmentioning

confidence: 99%

“…Another original paper, Sheeba and Krishnan [9] proposed a way to deal with classifying students' learning style based on their learning behavior. This approach is based on a decision tree classifier for the development of significant rules which are required for accurately distinguishing learning styles.…”

Section: Related Workmentioning

confidence: 99%

“…Log files: this source collects users' behavior when they are interacting with MOOC. It is possible, in this case, to obtain such information as the number of visits and the time spent in several learning objects such as content, outlines, self-assessments, exercises, quizzes, forums, questions, navigation, and examples [9,10,[12][13][14][15][16].…”

mentioning

confidence: 99%

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Use of Deep Multi-Target Prediction to Identify Learning Styles

2020

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Featured Application: Our results can be applied to identifying of students' learning style providing adaptation to e-learning systems.Abstract: It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students' learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students' learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder-Silverman learning styles model to improve students' learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.

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Section: Methodsmentioning

confidence: 99%

Section: Learning Styles Model Selectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Use of Deep Multi-Target Prediction to Identify Learning Styles

2020

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“…Furthermore, some recently published papers which have announced developed findings of the considered future of BL is presented. These findings have been introduced at the four very recent published research papers : [44], [45], [46], and [47]. .…”

Section: Second Motivation: This Motivation Consider Mainly the Questmentioning

confidence: 99%