Grouping of Success Levels in E-Learning Learning Factors: Approaches with Machine Learning Algorithm

The purpose of this study is to obtain the results of the modeling process on grouping the results of student learning, and to produce student success rates, while to find the results of the accuracy level of student learning success based on E-Learning with the Support Vectore Machine (SVM) method. In this grouping, there are 5 clusters. Furthermore, the process of counting can be as many as 2 iterations, namely getting the final result in the form of Cluster-1 with a total of 10 students, cluster-2 with a total of 45 students, cluster-3 with a total of 22 students, cluster 4 with a total of 13 students, and the next is cluster-5 with a total of 19 students. Then the results of the resulting process with a total of 5 types of clusters, namely from students who get the highest results to the lowest. In addition, this study also looks for the level of accuracy in e-learning student learning processes using the Support Vectore Machine (SVM) method, the accuracy results obtained are 90.91%, while the AUC results are 82.81%. then the value of the calculated accuracy rate can be classified as accuracy with the predicate result that is good.

[1]  S. Eze,et al.  Factors influencing the use of e-learning facilities by students in a private Higher Education Institution (HEI) in a developing economy , 2020 .

[2]  Aditya Khamparia,et al.  SVM and PCA Based Learning Feature Classification Approaches for E-Learning System , 2018, Int. J. Web Based Learn. Teach. Technol..

[3]  Selma Vonderwell,et al.  Factors that Influence Participation In Online Learning , 2005 .

[4]  Sugiono,et al.  Prediction of Evaluation Result of E-learning Success Based on Student Activity Logs With Selection of Neural Network Attributes Base on PSO , 2020 .

[5]  Badrul H. Khan,et al.  Web-Based Instruction (WBI): An Introduction. , 1998 .

[6]  Areej Alasiry,et al.  Deep Learning: The Impact on Future eLearning , 2020, Int. J. Emerg. Technol. Learn..

[7]  Shenming Gu,et al.  An improved Learning Evaluation system based on SVM for E-learning , 2012, 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI).

[8]  Elin Panca Saputra,et al.  Pengelompokan Perilaku Mahasiswa Pada Perkuliahan E-Learning dengan K-Means Clustering , 2019 .

[9]  Dinesh Kumar Saini,et al.  Investigation of Technological Tools used in Education System in Oman , 2019, Social Sciences & Humanities Open.

[10]  Alfonso González-Briones,et al.  Artificial neural network analysis of the academic performance of students in virtual learning environments , 2021, Neurocomputing.

[11]  Shreya Banerjee,et al.  Empirical evaluation of K-Means, Bisecting K-Means, Fuzzy C-Means and Genetic K-Means clustering algorithms , 2015, 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE).

[12]  I. Ioniță.  Data mining technique for e-learning , 2016 .

[13]  Miin-Shen Yang,et al.  A Feature-Reduction Multi-View k-Means Clustering Algorithm , 2019, IEEE Access.

[14]  Wen Gong,et al.  Application research of support vector machine in E-Learning for personality , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[15]  H. Baber.  Determinants of Students’ Perceived Learning Outcome and Satisfaction in Online Learning during the Pandemic of COVID19 , 2020, Journal of Education and e-Learning Research.

[16]  Aishan Wumaier,et al.  Study and Implementing K-mean Clustering Algorithm on English Text and Techniques to Find the Optimal Value of K , 2018, International Journal of Computer Applications.

[17]  Ridwansyah Ridwansyah,et al.  Prediction of Student Graduation Time Using the Best Algorithm , 2019, Indonesian Journal of Artificial Intelligence and Data Mining.

[18]  Faria Kanwal,et al.  Factors Affecting E-Learning Adoption in Developing Countries–Empirical Evidence From Pakistan’s Higher Education Sector , 2017, IEEE Access.

[19]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .