Prediction of Student’s Academic Performance using k-Means Clustering and Multiple Linear Regressions

Omolewa, Oladele Tinuke; Oladele, Aro Taye; Adegun, Adekanmi Adeyinka; Ogundokun, Roseline Oluwaseun

doi:10.36478/jeasci.2019.8254.8260

Cited by 15 publications

(9 citation statements)

References 4 publications

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“…It is recommended to use better models and software to analyse the scores [ 18 ]. Omolewa et al analysed evaluation activities in terms of pedagogical elements and constructed a new evaluation model in terms of objectives, form, and content [ 19 ]. Wiechetek and Pastuszak argue that the evaluation of students' academic performance should tend to be diversified, focusing not only on their creativity, but also on their behavioral habits and values [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Wang

2022

Computational Intelligence and Neuroscience

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Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained in student performance, thus limiting the impact of student performance evaluation on teaching and learning management. Data mining is regarded as the backbone technology for future information processing, and it introduces a new concept to the way humans use data. Schools must analyse and evaluate the performance of students in the same grade level and secondary school in a timely and staged manner. Clustering is a type of data mining that uses similarity rules to classify sample data into groups with a high degree of similarity. To address the difficulties caused by the wide variation in course difficulty in student performance evaluation, a method based on the K-means clustering algorithm is proposed. The K-means algorithm and the improved K-means algorithm with student information are investigated. The test results showed that the K-means clustering algorithm, the improved algorithm in this paper, and the fast global mean clustering algorithm all cluster the same randomly generated data set with noisy points, but the clustering time of the algorithm in this paper is only 0.04, which has obvious advantages. As a result, the clustering algorithm-based higher education management and student performance evaluation mechanism provides some insights for future research on student learning patterns. It is hoped that instructional administrators will gain a better understanding of students’ learning characteristics so that they can better guide their teaching.

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Section: Related Workmentioning

confidence: 99%

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Wang

2022

Computational Intelligence and Neuroscience

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“…Pengelompokkan data siswa menggunakan metode K-Means Clustering dilakukan berdasarkan nilai ujian semester mata pelajaran Ujian Nasiona [15]. Pengelompokan terhadap data untuk melihat dan menciptakan kelas objek-objek yang mempunyai kemiripan juga dapat dilakukan dengan K-Means Clustering [16].…”

Section: Pendahuluanunclassified

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

Elda

Defit

Yunus

et al. 2021

jidt

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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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“…Omolewa et al [21] a model was developed to predict student performance with multiple linear regressions. The clustering of data was achieved using k-means clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating learning outcomes in engineering education with data mining

Mahboob

Ali

Laila

2020

Comp Applic In Engineering

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Higher education institutions are catching up on their high competition and challenges are in their analysis productivity. The major challenge is to monitor and analyze student progress through learning outcomes in the curriculum. One of the approaches is the outcome-based education (OBE) model to deal with learning outcomes. OBE is an integral part of higher education institutions. The OBE system is a key step for accreditation in engineering education. OBE focuses on a student-centered approach. The OBE is not restricted to welldefined teaching strategies or direct evaluations but also encompasses indirect evaluations to help students achieve the intended outcomes. In this investigation, engineering students' data have been analyzed forming three distinct clusters to group students according to best, average, and worst achievement of learning outcomes in two different computer engineering courses generally taught in the early semesters in higher education institutions. A data mining clustering approach is used to segment students using k-means and k-medoids techniques. Clustering can be regarded as a data modeling technique that provides summary data that interact with multiple disciplines and plays an important role in a wide range of computer applications. The investigation comprises of two parts for analysis: one part of the analysis is the mid-term and final exam scores, the quiz and assignment results, the laboratory results, and the evaluation, together with the learning outcomes achieved, and the other part is the comparative analysis of learning outcomes achieved in both engineering courses clustering with the best, average, and worst attainments, respectively. In this investigation, the results obtained from clustering data points show that the same group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoid clustering for one course. On the other hand, a diverse group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoids clustering for another course.

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Prediction of Student’s Academic Performance using k-Means Clustering and Multiple Linear Regressions

Cited by 15 publications

References 4 publications

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

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

Investigating learning outcomes in engineering education with data mining

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