Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Aldabbagh, Ghadah; Alghazzawi, Daniyal; Hasan, S. S.; Alhaddad, Mohammed J.; Malibari, Areej; Cheng, Li

doi:10.1155/2020/6097167

Cited by 5 publications

(4 citation statements)

References 46 publications

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“…Mobile learning [37], [38] Learning style E-learning and traditional educational [39] Learning style, initial knowledge and motivation E-learning [40] Learning style, knowledge level, prior knowledge, learners' preferences E-learning -DAHS [41] Cognitive styles; learning behaviour and browsing behaviour E-learning [42] Learning style and learning motivation E-learning [43] Learning style and cognitive level…”

Section: Referencesmentioning

confidence: 99%

Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

Halim¹,

Mohemad²,

Ali³

2023

IJETAE

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Adaptive learning allows students to learn effectively based on their abilities and characteristics at their own pace. Currently, numerous SLRs exist regarding students' adaptive learning characteristics, analyzed from various perspectives by previous researchers. However, it is essential to note that the findings of these studies are based on analyses conducted between 2010 and 2020. This SLR aims to extend and bridge the gaps in identifying essential student characteristics for implementing an adaptive learning system. It specifically focuses on the most recent five-year period, from 2018 to 2022. This study chose 39 articles according to the specified inclusion and exclusion criteria. The findings from the SLR indicate that learning style is the most commonly used element of adaptation in the reviewed articles, followed by knowledge characteristics, cognitive traits, student preference, and motivation. This SLR also revealed that most of the reviewed articles used more than one student's characteristics in modeling the student model, and results show that educators integrated online learning for implementing adaptive learning in the teaching and learning process. The ILS instrument, which is traditional detection, is widely used in collecting learning style data, and besides FSLSM, other learning style models, including VARK, Kolb, and Honey & Mumford, are used to assign students' learning style preferences.

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Section: Referencesmentioning

confidence: 99%

Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

Halim¹,

Mohemad²,

Ali³

2023

IJETAE

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“…Aldabbagh et al 19 proposed an adaptive optimization‐based neural network (AONN) model for optimal learning behavior prediction. Features of browsing behavior and e‐learning behavior are extracted and integrated into the artificial neural network (ANN) input.…”

Section: Related Workmentioning

confidence: 99%

Personalized ontology and deep training tree‐based optimal gated recurrent unit‐recurrent neural network for prediction of students' behavior

Fernandez

Ramana²,

Shabana³

et al. 2022

Concurrency and Computation

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Summary Digital libraries are now widely used by students all around the world due to the hassle‐free services it offers in the form of digitized study materials, ebooks, multimedia content and so forth. However, to increase user satisfaction and allegiance, a personalized digital library needs to be deployed to offer customized services to the users. To accomplish this objective, this article presents a novel scheme to design personalized digital libraries via the usage of the Protégé editor and machine learning techniques. The Protégé 4.3 tool is used to generate the digital library ontologies and identify the interrelated concepts. Domain knowledge acquisition, ontology organization, ontology elaboration, ensuring the consistency of the information, and ontology validation. The gated recurrent unit‐recurrent neural network (GRU‐RNN) with a deep training tree (DTT) is used to predict the different user behavior styles such as cognitive behavior, learning speed behavior, sedentary behavior, and aggressive behavior. The DTT is mainly incorporated to overcome the vanishing gradient issue associated with the GRU‐RNN and used in the place of gradient‐based optimization. A black widow optimization approach is employed to update the weights of the GRU‐RNN network in order to increase its accuracy. The efficiency of the proposed methodology is analyzed by using different performance metrics such as F‐score, accuracy, loss, precision, and recall scores. The results show that the personalized digital library created can be efficient in terms of usability.

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“…A substantial amount of work has been done on the recognition of static gestures using cameras. For static hand, gesture recognition features are extracted via different methods [27][28][29][30][31]. Features can be extracted using the full hand or by using only the fingers of the hand.…”

Section: Hgr Through Cameramentioning

confidence: 99%

Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

et al. 2021

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Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directional image methods. The fused method produced greater extracted gesture recognition accuracy. In the proposed system, hand gesture recognition (HGR) is done via several different methods, namely, (1) HGR via point-based features, which consist of (i) distance features, (ii) angular features, and (iii) geometric features; (2) HGR via full hand features, which are composed of (i) SONG mesh geometry and (ii) active model. To optimize these features, we applied gray wolf optimization. After optimization, a reweighted genetic algorithm was used for classification and gesture recognition. Experimentation was performed on five challenging datasets: Sign Word, Dexter1, Dexter + Object, STB, and NYU. Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system. The classification results of the reweighted genetic algorithm were compared with Artificial Neural Network (ANN) and decision tree. The developed system plays a significant role in healthcare muscle exercise.

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Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Cited by 5 publications

References 46 publications

Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

Personalized ontology and deep training tree‐based optimal gated recurrent unit‐recurrent neural network for prediction of students' behavior

Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

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