Clustering students into groups according to their learning style

Pasina, Irene; Bayram, Goze; Labib, Wafa; Abdelhadi, Abdelhakim; Nurunnabi, Mohammad

doi:10.1016/j.mex.2019.09.026

Cited by 25 publications

(14 citation statements)

References 5 publications

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“…Finally, a study by [39] discovered among industrial engineering students in Brazil the trend towards sensing (70 per cent), visual (73 per cent), and active (66 per cent) poles. Although there is the recent proposal of [32], we do not find student cluster reports associated with their learning styles based on the ILS, hence the importance of these results.…”

Section: Discussioncontrasting

confidence: 86%

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Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

2021

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In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a predictive model to detect undergraduates’ learning style profiles quickly. The methodological design consists of applying a k-means clustering algorithm to identify the students’ learning style profiles and a decision tree C4.5 algorithm to predict the student’s membership to the previously identified groups. A cluster sample design was used with Chilean engineering students. The research result is a predictive model that, with few questions, detects students’ profiles with an accuracy of 82.93%; this prediction enables a rapid adjustment of teaching methods in a hybrid learning environment.

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Section: Discussioncontrasting

confidence: 86%

“…Following the proposal of [32], we used a k-means clustering algorithm to categorise students based on their style learning preferences. Table 2 and Figure 2 show the cluster analysis results.…”

Section: Cluster Analysismentioning

confidence: 99%

Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

2021

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“…Nowadays, the artificial intelligence methodologies applied in education are more and more popular [4,[19][20][21][22]. Ammar Almasri et al [21] have used data mining techniques in education (EDM) to build an understandable model.…”

Section: Artificial Intelligence Methodologies In Educationmentioning

confidence: 99%

“…The accuracy, precision, recall and f1-score measurement are used to evaluate the model with the highest classification results up to 96.96%. Irene Pasina et al [22] have used the hierarchal clustering algorithms to group students based on their learning style. Their experiment in engineering education is limited in Saudi Arabia.…”

Section: Artificial Intelligence Methodologies In Educationmentioning

confidence: 99%

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

2020

Education Sciences

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The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.

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“…Metode K-Means Clustering and Multiple Linear Regressions (MLR) dengan mengambil data dari UCI (University California Irvine), dimana set data didasarkan dari nilai matematika siswa, nilai ulangan, kuis, dan tugas siswa sebagai faktor utama dalam memprediksi kinerja akademik siswa [17]. Algoritma Clustering K-Means dan Algoritma Greedy, dengan data 37 siswa menghasilkan 5 cluster kualitas siswa, sehingga dari cluster tersebut terbentuk kelompok kerja siswa untuk pemerataan kualitas siswa [18].…”

Section: Pendahuluanunclassified

Klasterisasi Penempatan Siswa yang Optimal untuk Meningkatkan Nilai Rata-Rata Kelas Menggunakan K-Means

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The implementation of learning by teachers can measure the quality of schools and students. Schools with diverse student backgrounds need to take strategic steps in managing learning to get optimal learning outcomes. Good learning designs and techniques can motivate students' interest in learning. The teacher's role is very important in managing learning to create an effective teaching and learning process. Data Mining or also known as Knowledge Discovery in Database (KDD) is the process of extracting knowledge from large data to find new patterns to get new knowledge and information. Data Mining technology is used to explore existing knowledge in the database. One of the methods used in data mining is clustering with the K-Means algorithm. This study aims to conduct student clustering to obtain a balanced class composition in order to improve the quality and student learning outcomes as seen in the increasing in the class average score. The data processed in this study came from the main school data as many as 90 students of the XI class of Computer Network Engineering Skills Competency at SMKN Negeri 2 Padang Panjang in the 2020/2021 school year. The variables used in data processing are student scores, parents' income and the distance from where students live to school. The student clustering calculation using K-Means succeeded in grouping 90 students into 3 clusters where cluster 1 totaled 47 students, cluster 2 totaled 10 students and cluster 3 totaled 33 students. Each member of the cluster will be divided evenly into 3 groups studying to get a balanced class composition. This research can be used as a basis for decision making by schools in clustering student placements to improve learning outcomes. By the increasing in the grade point average, the school average score will also increased.

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Clustering students into groups according to their learning style

Abstract: Graphical abstract

Cited by 25 publications

References 5 publications

Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

Klasterisasi Penempatan Siswa yang Optimal untuk Meningkatkan Nilai Rata-Rata Kelas Menggunakan K-Means

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