Student graduation time prediction using intelligent K-Medoids Algorithm

Cahaya, Leonardo; Hiryanto, Lely; Handhayani, Teny

doi:10.1109/icsitech.2017.8257122

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…Other factors influencing the range of graduation times in each cluster include academic leave or extending the thesis completion period. The average prediction accuracy of 99.58 [14] is obtained by k-folding 240 data into 5 subsets. Predicting student graduation based on 667 tests completed by the author of the training data.…”

Section: Introductionmentioning

confidence: 99%

K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation

Hidayati¹,

Hermawan

2021

JEATech

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K-Nearest Neighbor (K-NN) algorithm is a classification algorithm that has been proven to solve various classification problems. Two approaches that can be used in this algorithm are K-NN with Euclidean and K-NN with Manhattan. The research aims to apply the K-NN algorithm with Euclidean and K-NN with Manhattan to classify the accuracy of graduation. Student graduation is determined by the variables of gender, major, number of first-semester credits, number of second-semester credits, number of third-semester credits, grade point on the first semester, grade point on the second semester, grade point on the third semester, and age. These variables determine the accuracy of student graduation, timely or untimely. The implementation of the K-NN algorithm is carried out using Rapidminer software. The results were obtained after testing 380 training data and 163 testing data. The best accuracy system was achieved at K=7 with a value of 85.28%. The two algorithmic approaches did not affect the accuracy of the results. Furthermore, the addition of the value of K did not completely affect the accuracy.

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Section: Introductionmentioning

confidence: 99%

K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation

Hidayati¹,

Hermawan

2021

JEATech

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“…A range of supervised and unsupervised techniques have been deployed in the existing literature to predict the time students take to graduate. Cahaya, Hiryanto, and Handhayani (2017) deployed the k-Medoids clustering algorithm to create clusters based on intracluster similarity. Overall, seven clusters were formed based on the data of nearly 250 graduate students and labelled according to the approximate time of students' graduation.…”

Section: Prediction Of Students Graduation Timementioning

confidence: 99%

Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk

Brdesee

Alsaggaf

Aljohani

et al. 2022

International Journal on Semantic Web and Information Systems

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Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends to predict the students at-risk of low performances during an on-going course, those at-risk of graduating late than the tentative timeline and predicting the capacity of students in a campus. The data constitutes of demographics, learning, academic and educational related attributes which are suitable to deploy various machine learning algorithms for the prediction of at-risk students. For class balancing, Synthetic Minority Over Sampling Technique, is also applied to eliminate the imbalance in the academic award-gap performances and late/timely graduates. Results reveal the effectiveness of the deployed techniques with Long short-term Memory (LSTM) outperforming other models for early prediction of at-risk students. The main contribution of this work is a machine learning approach capable of enhancing the academic decision making related to student performance.

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“…As defined by [28], clustering is when the data is divided into groups of similar objects. In some applications, clustering is also known as data segmentation as it divides huge data set into many groups, where each group shares a similar characteristic [29,30]. On top of that, clustering nowadays is commonly deployed in education Data mining to group students based on their characteristics.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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Student graduation time prediction using intelligent K-Medoids Algorithm

Cited by 8 publications

References 3 publications

K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation

K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation

Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

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