How to cite (IEEE): H. Park, & S. Yoo "Early Dropout Prediction in Online Learning of University using Machine Learning," JOIV : International Journal on Informatics Visualization, vol. 5, no. 4, , pp. 347-353, Dec. 2021. https://doi.org/10.30630/joiv.5.4.732

How to cite (APA): Park, H., & Yoo, S. (2021). Early Dropout Prediction in Online Learning of University using Machine Learning. JOIV : International Journal on Informatics Visualization, 5(4), 347-353. https://doi.org/10.30630/joiv.5.4.732

How to cite (Chicago): Park, Hee Sun, AND Yoo, Seong Joon. "Early Dropout Prediction in Online Learning of University using Machine Learning" JOIV : International Journal on Informatics Visualization [Online], Volume 5 Number 4 (25 December 2021) https://doi.org/10.30630/joiv.5.4.732

How to cite (Vancouver): Park H, & Yoo S . Early Dropout Prediction in Online Learning of University using Machine Learning. JOIV : International Journal on Informatics Visualization [Online]. 2021 Dec; 5(4):347-353. https://doi.org/10.30630/joiv.5.4.732

How to cite (Harvard): Park, H., & Yoo, S. ,2021. Early Dropout Prediction in Online Learning of University using Machine Learning. JOIV : International Journal on Informatics Visualization, [Online] 5(4), pp. 347-353. https://doi.org/10.30630/joiv.5.4.732

How to cite (MLA8): Park, Hee Sun, & Seong Joon Yoo. "Early Dropout Prediction in Online Learning of University using Machine Learning." JOIV : International Journal on Informatics Visualization [Online], 5.4 (2021): 347-353. Web. 30 Jun. 2022 , https://doi.org/10.30630/joiv.5.4.732

BibTex Citation Data :

@article{JOIV732,
    	author = {Hee Sun Park and Seong Joon Yoo},
    	title = {Early Dropout Prediction in Online Learning of University using Machine Learning},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {5},
    	number = {4},
    	year = {2021},
    	keywords = {Dropout prediction; online learning; machine learning; deep learning.},
    	abstract = {Recently, most universities plan to open or open online learning courses, but the problem of  dropout of online learning  is still a problem for universities. Online learning has the advantage of being able to receive education anytime, anywhere, but it is true that the dropout rate is higher than offline classes because you have to manage and control your own study time without the help of a professor or manager. Therefore, it is very important for professors and managers to support students in a timely act to avoid the risk of dropout of university online classes. This study used the access log data recorded in the Learning Management System (LMS) and the learner's statistical information and calculated data, and aims to present predictive algorithms suitable for online learning dropout early prediction systems at universities. This study features a 7-year online learning history log data recorded in the Cyber University LMS system to overcome the data count limitations of existing studies and predict the risk of drop-out during the learning period.  The characteristics of the data you utilized were used to validate the availability of predictive models by applying learner statistical information, number of system connections, number of lectures, previous semester grade data, machine learning based decision tree, arbitrary forest (RF), support vector machine (SVM) and deep learning (DNN). Studies show that random forest (RF) algorithms have the best prediction and performance, and deep learning algorithms also apply to learning management (LMS) systems.},
        	issn = {2549-9904},    	pages = {347--353},    	doi = {10.30630/joiv.5.4.732},
        	url = {https://joiv.org/index.php/joiv/article/view/732}
    }
    

Refworks Citation Data :

@article{{JOIV}{732}, author = {Park, H., Yoo, S.}, title = {Early Dropout Prediction in Online Learning of University using Machine Learning}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {5}, number = {4}, year = {2021}, doi = {10.30630/joiv.5.4.732}, url = {} }