Student Engagement Level in an e-Learning Environment: Clustering Using K-means

Moubayed, Abdallah; Injadat, MohammadNoor; Shami, Abdallah; Lutfiyya, Hanan

doi:10.1080/08923647.2020.1696140

Cited by 120 publications

(91 citation statements)

References 31 publications

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“…The results of the study reveal that the distribution of the data is normal, which shows that there is a homogeneous perception of the student body on the application of the e-learning method in mathematics teaching. On the other hand, a very significant influence was detected among all the dimensions of the study, which shows that the application of the e-learning method affects all the dimensions analyzed, although with different degrees of association (Moubayed et al, 2020). In this case, the dimensions participation-autonomy, results-resolution, decision-concepts and decision-results obtain higher values of association, suggesting a direct influence between the results with respect to the students' resolution and decisions.…”

Section: Discussionmentioning

confidence: 78%

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The e-Learning Method for Teaching Mathematical Content in the COVID-19 Era

Huamán-Romaní¹

2021

TURCOMAT

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The technological progress in the social field in a sudden way, specifically in the pedagogical field due to the spread of COVID-19, has had to force and use new teaching and learning strategies. As one of these many teaching processes is the method known as e-learning, which has become more valuable and very important in these times due to the rapid spread of the COVID-19 pandemic. The research method applied is descriptive, correlational, cross-sectional and predictive. The sample applied in this research is 608 students of the 5th grade of secondary education in the province of Sullana, Piura region of Peru. The results reveal the existence of a very significant influence in almost all the dimensions of the study. The dimensions with the highest association values are participation-autonomy, outcome resolution, decision concepts and decision outcomes. The dimensions with the lowest strength of association are undoubtedly the motivation rating, which can be interpreted as meaning that motivation does not have a strong influence on the results. It can be concluded that the application of the e-learning method in the teaching of 5th grade high school students in the area of mathematics is positively valued, although a strong incidence of the method on the grades is not observed, due to the fact that a lower percentage of students were unaware of the application of information and communication technologies for online learning. In addition, women tend to rate autonomy and participation more positively than men when the aforementioned teaching method is applied.

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Section: Discussionmentioning

confidence: 78%

“…The e-learning method can lead to a space-time gap for teachers (Cerezo et al, 2020). To avoid this, it is necessary to offer personalized teaching environments, trying to motivate and encourage their own learning process and pace (Moubayed et al, 2020). Another counter-indication is the digital divide…”

Section: Introductionmentioning

confidence: 99%

The e-Learning Method for Teaching Mathematical Content in the COVID-19 Era

Huamán-Romaní¹

2021

TURCOMAT

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“…The recommendation is not based on conceptual considerations but on estimations based on the relationship between the entities. Such an approach has proven to be an effective solution for various educational problems; for example, in the case of a course recommender such as the one used at Stanford University (Koutrika, Bercovitz, Kaliszan, Liou, & Garcia-Molina, 2009) or the University of Berkeley (Pardos, Fan, & Jiang, 2019), for matching students as partners for cooperative learning (Moubayed, Injadat, Shami, & Lutfiyya, 2020), or for providing suggestions on scholarly research papers for students (Porcel, Ching-López, Lefranc, Loia, & Herrera-Viedma, 2018). Other systems help to find resources for teachers in larger databases (Ruiz-Iniesta, Jimenez-Diaz, & Gomez-.…”

Section: Discussionmentioning

confidence: 99%

Recommender in AI-enhanced Learning: An Assessment from the Perspective of Instructional Design

Kerres

Buntins

2020

Open Education Studies

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As tools for AI-enhanced human learning, recommender systems support learners in finding materials and sequencing learning paths. The paper explores how these recommenders improve the learning experience from a perspective of instructional design. It analyzes mechanisms underlying current recommender systems, and it derives concrete examples of how they operate: Recommenders are either expert-, criteria-, behavior-, or profile-based or rely on social comparisons. To verify this classification of five different mechanisms, we analyze a set of current publications on recommenders and find all the identified mechanisms with profile-based approaches as the most common. Social recommenders, though highly attractive in other sectors, reveal some drawbacks in the context of learning. In comparison, expert-based recommendations are easy to implement and often stand out as simple but effective ways for suggesting learning materials and learning paths to learners. They can be combined with other approaches based on social comparisons and individual profiles. The paper points out challenges in studying recommenders for learning and provides suggestions for future research.

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“…Penelitian menggunakan metode K-Means untuk menganalisa pelanggan potensial pada SPS Motor Honda Lombok Timur [12]. Serta dapat menganalisa segmen pasar handphone berbasis android [13], segmen pasar IndiHome berbasis python [14] dan tingkat keterlibatan siswa dalam belajar e-Learning [15].…”

Section: Pendahuluanunclassified

Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

Fakhri

Defit

Sumijan

2021

jidt

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Knowledge Discovery in Database (KDD) is a structured analysis process aimed at getting new and correct information, finding patterns from complex data, and being useful. Data mining is at the core of the KDD process. Clustering is a data mining method that is suitable for optimizing library services because it can cluster books effectively and efficiently, with the K-Means algorithm data can be clustered and information from each centroid value of each cluster. Library services can optimize the placement of books so that students can quickly find books according to their reading interest more effectively and can be attracted to other books because they are in one grouping. Meanwhile, the library can prioritize the procurement of the next book. Optimization of library services in the cluster using the K-Means method. Clustering interest in reading has the criteria for the number of books available, borrowed books, and the length of time the books are borrowed. The book data is clustered into 3, namely very interested, in demand, and less desirable. After doing the calculation process from 40 samples of book types, it resulted in 6 iterations, and the final results were 3 clustering, namely cluster 1 of 4 books that were of great interest, cluster 2 of 20 books that were of interest, and cluster 3 of 16 books that were less desirable. This research can be used as a recommendation reference for optimizing library services both for the layout and procurement of books by prioritizing the types of books that are of great interest.

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Student Engagement Level in an e-Learning Environment: Clustering Using K-means

Cited by 120 publications

References 31 publications

The e-Learning Method for Teaching Mathematical Content in the COVID-19 Era

The e-Learning Method for Teaching Mathematical Content in the COVID-19 Era

Recommender in AI-enhanced Learning: An Assessment from the Perspective of Instructional Design

Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

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