Extending the Student’s Performance via K-Means and Blended Learning

Parveen, Zahida; Arabia, Hail Saudi; Alphones, Anushya; Naz, Samina

doi:10.24032/ijeacs/0204/03

Cited by 9 publications

(3 citation statements)

References 2 publications

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“…Penelitian ini bertujuan untuk mengelompokkan hasil belajar mahasiswa di masa Pandemi Covid-19, berdasarkan penelitian terdahulu yang telah dilakukan tentang clustering dengan Algoritma K-Means [5][13] [16] [17] tidak secara spesifik membahas penentuan jumlah kelompok berdasarkan nilai DBI yang maksimal, sehingga memerlukan peningkatan kinerja Algoritma K-Means melalui Optimize parameter, sehingga penelitian ini dilakukan menggunakan Algoritma K-Means. Dalam penentuan klasternya dapat dibuat sebanyak dua, tiga, empat dan seterusnya, dimana setiap klaster mempunyai karakteristik yang sama [3].…”

Section: Pendahuluanunclassified

Clustering Hasil Belajar Menggunakan Algoritma K-Means Dengan Optimize Parameter Dalam Menjamin Mutu Pendidikan Di Era Pandemi Covid-19

Rifai¹,

Dikananda²,

Juliane³

2022

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The COVID-19 pandemic has greatly affected the learning environment, with the excuse of stopping the spread of COVID-19 infection. Teaching and learning activities that have generally been completed on campus face-to-face now have to be transferred to distance learning. However, one of the drawbacks of implementing distance learning is that it makes students less active, so that KBM feels tiring. The purpose of this study is to classify student learning outcomes during the COVID-19 Pandemic. The method used is the Knowledge Discovery Database (KDD) using the K-Means Algorithm. The number of clusters selected is the number of clusters with the smallest Davies Bouldin Index (DBI). The results of this study obtained 3 clusters with a DBI value of 1.379 and a centroid distance of 0.342. Cluster_1 is the data group with the highest quality index, Cluster_2 is the data group with the second highest quality index, and Cluster_0 is the data group with the lowest quality index of all clusters. By knowing the clusters of PJJ learning outcomes, it will make it easier for universities to take improvement steps to improve the quality of learning in accordance with the characteristics of each existing cluster

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Section: Pendahuluanunclassified

Clustering Hasil Belajar Menggunakan Algoritma K-Means Dengan Optimize Parameter Dalam Menjamin Mutu Pendidikan Di Era Pandemi Covid-19

Rifai¹,

Dikananda²,

Juliane³

2022

bits

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“…The competency of each student was associated with multiple clusters, which were used to build a rule set for detecting weak students. Parveen et al (2017) employed K -means to create GPA groups to identify dunce students who need to be remedied. Research by Shankar et al (2016) clustered students by using attributes such as average grade, the number of participated events and the number of attended chapters.…”

Section: Related Workmentioning

confidence: 99%

Hybrid data analytic technique for grading fairness

Banditwattanawong

Jankasem²,

Masdisornchote

2022

DTA

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PurposeFair grading produces learning ability levels that are understandable and acceptable to both learners and instructors. Norm-referenced grading can be achieved by several means such as z score, K-means and a heuristic. However, these methods typically deliver the varied degrees of grading fairness depending on input score data.Design/methodology/approachTo attain the fairest grading, this paper proposes a hybrid algorithm that integrates z score, K-means and heuristic methods with a novel fairness objective function as a decision function.FindingsDepending on an experimented data set, each of the algorithm's constituent methods could deliver the fairest grading results with fairness degrees ranging from 0.110 to 0.646. We also pointed out key factors in the fairness improvement of norm-referenced achievement grading.Originality/valueThe main contributions of this paper are four folds: the definition of fair norm-referenced grading requirements, a hybrid algorithm for fair norm-referenced grading, a fairness metric for norm-referenced grading and the fairness performance results of the statistical, heuristic and machine learning methods.

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“…Any weak students were identified before the final exam to reduce the ratio of fail students. Research by Parveen et al [4] employed K-means to create 9 groups of GPAs: exceptional, excellent, superior, very good, above average, good, high pass, pass, and fail. Students whose GPAs belonged to the exceptional and the fail groups were called gifted and dunce, respectively.…”

Section: Related Workmentioning

confidence: 99%

On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

Banditwattanawong

Masdisornchote

2021

Applied Computational Intelligence and Soft Computing

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Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attractive to the norm-referenced grading in general. This paper also investigates two popular clustering methods, K-means and partitioning around medoids. The experiment relied on the data sets of various score distributions and a metric, namely, Davies–Bouldin index. The comparative evaluation reveals that our algorithm overall outperforms the other three methods and is appropriate for all kinds of data sets in almost all cases. Our findings however lead to a practically useful guideline for the selection of appropriate grading methods including both clustering methods and z score.

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Extending the Student’s Performance via K-Means and Blended Learning

Cited by 9 publications

References 2 publications

Clustering Hasil Belajar Menggunakan Algoritma K-Means Dengan Optimize Parameter Dalam Menjamin Mutu Pendidikan Di Era Pandemi Covid-19

Clustering Hasil Belajar Menggunakan Algoritma K-Means Dengan Optimize Parameter Dalam Menjamin Mutu Pendidikan Di Era Pandemi Covid-19

Hybrid data analytic technique for grading fairness

On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

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