Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles

Aissaoui, Ouafae El; Madani, Yasser El Alami El; Oughdir, Lahcen; Allioui, Youssouf El

doi:10.1016/j.procs.2019.01.012

Cited by 53 publications

(34 citation statements)

References 9 publications

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“…It is in accordance with the opinion of Aissaoui: Due to personality and environmental factors, each student has his own preferred ways of learning, for example, when doing an experiment, some students can understand by following verbal instructions, while others have to physically practice the experiment themselves. These differences in students' learning styles should be considered by the educational systems to enhance the learning process [8].…”

Section: Introductionmentioning

confidence: 99%

Understanding of mathematical concepts through cooperative learning, and learning styles

Virgana

2019

EduLearn

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The results of the mathematics learning of junior high school students have not been satisfactory. This is due to the low understanding of students' mathematical concepts. The purpose of this research is to identify the influence of cooperative learning model and learning style towards the understanding of the mathematical concepts of Junior High School students. The method is factorial experiments with 2 x 3 and sample of the study is 60 students of 8th grade junior high school. The analysis results of variance (ANOVA) indicate the following: the use of cooperative learning model of type Student Team Achievement Division influenced the understanding of the mathematical concepts significantly, there is an significant influence of the learning style towards an understanding of mathematical concepts, and there is also a significant influence of the interaction of cooperative learning model of type Student Team Achievement Division and learning style towards the understanding of mathematical concepts. So it can be concluded that the cooperative learning model can improve understanding of students' mathematical concepts and learning styles is a individual differences of learning students in absorbing mathematics subject matter. We hope that this study can be beneficial for further research to improve student's achievements in mathematics.

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Section: Introductionmentioning

confidence: 99%

Understanding of mathematical concepts through cooperative learning, and learning styles

Virgana

2019

EduLearn

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“…The authors grouped learners’ behaviors into eight categories by clustering algorithm FCM (Fuzzy C Means) and matched each category with a learning preference type in FSLSM. Similar approaches also can be found in Aissaoui et al (2018), Aissaoui et al (2019).…”

Section: Learning Preference Evaluation Methodsmentioning

confidence: 58%

Learning preference: development in smart learning environments

Zhang

Hung

et al. 2021

IDD

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Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research. Findings After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments. Research limitations/implications This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account. Originality/value The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

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“…Testing the classification algorithm in this study in this conducted using a multi-class confusion matrix [46] n × n with n = 16, because it is used to analyze the classification of learning style detection containing 16 classes. If using a multiclass confusion matrix, the total number of false negatives (T F N ), false positives (T F P ), and true negative (T T N ) for each class number i will be calculated based on Generalized (24), (25), and (26). equations.…”

Section: Model Testingmentioning

confidence: 99%

“…According to [25], most studies detecting FSLSM learning styles group learning styles into eight combinations of learning styles. If observed from the FSLSM learning style model consisting of 4 dimensions with each dimension having two categories, then it is possible to have 16 combinations of learning styles.…”

Section: Introductionmentioning

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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Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles

Cited by 53 publications

References 9 publications

Understanding of mathematical concepts through cooperative learning, and learning styles

Understanding of mathematical concepts through cooperative learning, and learning styles

Learning preference: development in smart learning environments

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

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