Model detecting learning styles with artificial neural network

Hasibuan, Muhammad Said; Nugroho, Lukito Edi; Santosa, Paulus Insap

doi:10.3926/jotse.540

Cited by 42 publications

(22 citation statements)

References 19 publications

(22 reference statements)

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“…There are classifications techniques have been tried out to classify learning styles with a range of learning style models. These techniques sequence the learning activities and observe the learners behaviours with the system [20][21][22]. According to Dung, the courses are comprised of several topics with different learning contents which are referred as learning objects.…”

Section: Identification Of Learning Stylesmentioning

confidence: 99%

Learners Classification for Personalized Learning Experience in e-Learning Systems

Martin¹,

Francis²

2021

IJACSA

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The investigators are inspired by the increasing need and the demand for educational applications and the Learning Management Systems which provide learning objects centered on the learning style of the learners. The technique in which the learners acquire, process, gain the information is unique; these unique characteristics affect their learning process. Hence it is essential to consider and understand the uniqueness among the learners to deliver learner-centric learning objects. The investigators present a system to classify the learners based on the time spent by the learner on learning content of different types. The types of learning content are identified with the percentage of visual, auditory, read/write and kinesthetic in learning object. The prominent learning style called VARK (Visual, Auditory, Read/Write and Kinesthetic) is used to classify the learners. This system classifies the learner and recommends the learning objects based on their learning preference, it also facilitates the faculty members or the content creators to prepare and provide personalized learning objects based on the learning style of the learners.

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Section: Identification Of Learning Stylesmentioning

confidence: 99%

Learners Classification for Personalized Learning Experience in e-Learning Systems

Martin¹,

Francis²

2021

IJACSA

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“…erefore, it has the potential to emerge as the predominant education model. Hasibuan et al [26] carried out a knowledge generation process of research by using the latent semantic indexing method for identifying the personality traits that are possessed by each learner. Latent semantic indexing is a technique implemented to find meaning in a sentence via singular value decomposition.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.

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“…The research aimed at identifying VAK learning styles were carried out by [38] using the Decision Tree C4.5 algorithm on questionnaire data. Meanwhile, [39] predicted VAK learning styles using the artificial neural network (ANN) method is Latent Semantic Indexing.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Hidayat¹,

Wardoyo²,

Sn³

et al. 2020

IJACSA

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Learning Management System (LMS) is well designed and operated by an exceptional teaching team, but LMS does not consider the needs and characteristics of each student's learning style. The LMS has not yet provided a feature to detect student diversity, but LMS has a track record of student learning activities known as log files. This study proposes a detection model of student's learning styles by utilizing information on log file data consisting of four processes. The first process is pre-processing to get 29 features that are used as the input in the clustering process. The second process is clustering using a modified K-Means algorithm to get a label from each test data set before the classification process is carried out. The third process is detecting learning styles from each data set using the Naive Bayesian classification algorithm, and finally, the analysis of the performance of the proposed model. The test results using the validity value of the Davies-Bouldin Index (DBI) matrix indicate that the modified K-Means algorithm achieved 2.54 DBI, higher than that of original K-Means with 2.39 DBI. Besides having high validity, it also makes the algorithm more stable than the original K-Means algorithm because the labels of each dataset do not change. The improved performance of the clustering algorithm also increases the values of precision, recall, and accuracy of the automatic learning style detection model proposed in this study. The average precision value rises from 65.42% to 71.09%, the value of recall increases from 72.09% to 80.23%, and the value of accuracy increases from 67.06% to 71.60%.

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Model detecting learning styles with artificial neural network

Cited by 42 publications

References 19 publications

Learners Classification for Personalized Learning Experience in e-Learning Systems

Learners Classification for Personalized Learning Experience in e-Learning Systems

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

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

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