Clustering analysis of learning style on anggana high school student

Lailiyah, Siti; Yulsilviana, Ekawati; Andrea, Reza

doi:10.12928/telkomnika.v17i3.9101

Cited by 10 publications

(11 citation statements)

References 14 publications

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“…In this case, data analysis techniques were applied to assess the student-student and student-teacher interaction to see how the information extracted from clustering analysis can affect teaching strategies, especially those related to strategic group formation and school management ( Ponciano et al, 2020 ). Lailiyah et al (2019) benefited from clustering algorithms to identify student behavior and preferences in a high school context. The authors collected data from questionnaires and used traditional clustering algorithms (k-Means and Fuzzy C-Means) to aggregate students with similar characteristics.…”

Section: Resultsmentioning

confidence: 99%

“…Only two papers used the state of the art decision tree algorithms (Adaboost and XGBoost) ( Jiménez-Gómez et al, 2015 ; Lakkaraju et al, 2015 ). Moreover, the k-means algorithm was used in 75% of the papers related to clustering analysis ( Abadi et al, 2018 ; Lailiyah et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…Seven studies obtained the maximum score, and the vast majority of studies obtained grades 6 and 5 out of 7. The highest number of negative responses was found for ( Lailiyah et al, 2019 ).…”

Section: Methodsmentioning

confidence: 93%

See 2 more Smart Citations

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

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Learning analytics aims to analyze data from students and learning environments to support learning at different levels. Although learning analytics is a recent field, it reached a high level of maturity, especially in its applications for higher education. However, little of the research in learning analytics targets other educational levels, such as high school. This paper reports the results of a systematic literature review (SLR) focused on the adoption of learning analytics in high schools. More specifically, the SLR followed four steps: the search, selection of relevant studies, critical assessment, and the extraction of the relevant field, which included the main goals, approaches, techniques, and challenges of adopting learning analytics in high school. The results show that, in this context, learning analytics applications are focused on small-scale initiatives rather than institutional adoption. Based on the findings of this study, in combination with the literature, this paper proposes future directions of research and development in order to scale up learning analytics applications in high schools.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…It works by segregate n existing objects into k partitions (k ≤ n) to represent as a cluster so that object values in each cluster are more similar to one another than objects in different clusters. In K-means clustering, every cluster is represented by its centroid, which is calculated as the mean value of the data in that cluster [34]. The basic K-means algorithm is consisting of the following steps:…”

Section: A Tools Used and Techniquesmentioning

confidence: 99%

Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance

Al-Hagery¹,

Alzaid²,

Alharbi³

et al. 2020

IJITCS

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The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data.

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“…For a given data set X that contains multidimensional data points and a class K to be divided, Euclidean distance is defined as an indicator of similarity and group targets reduce the sum of squares of different objects; this means that it reduces [21]. K-Means algorithm is a widely used algorithm for identifying clusters because it has accurate calculations, easy to use and meets the needs of use because it is flexible to modify [22].…”

Section: K-means Algorithmmentioning

confidence: 99%

Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

Jaafar

Gaata

Jasim

2020

IJEECS

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<p>The recommendation system is an intelligent system gives recommendations to users to discover the best interesting items. The purpose of this proposed recommendation system is to develop a system to find the best electrical devices according to weather conditions and user preferences. The proposed solution relies on the characteristics of electrical appliances and their suitability to weather conditions in any city. The proposed solution is the first recommendation system combines devices properties, weather conditions, and user preferences using a new combination of algorithms. The clustering algorithms are the most applicable in the field of recommendation system. The proposed solution relies on a combination of Elbow method, proposed modified K-means and Silhouette algorithm to find the best number of clusters before starting the clustering process. Then calculate the weights for each cluster and compare them with the weather weights to find the required clusters sorted from the near to far according to a computed threshold. The empirical results showed that the proposed solution demonstrated a 94% accuracy to match the characteristics of the recommended devices with the climatic characteristics of the region and user preferences. The accuracy is measured using Silhouette algorithm.</p>

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Clustering analysis of learning style on anggana high school student

Cited by 10 publications

References 14 publications

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance

Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

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