Open AccessA deep learning analytics to facilitate sustainability of statistics education
Author(s) -
Tae Rim Lee,
AUTHOR_ID
Publication year - 2019
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52041/srap.19306
Subject(s) - learning analytics , computer science , random forest , machine learning , support vector machine , analytics , artificial intelligence , deep learning , predictive analytics , logistic regression , multidisciplinary approach , data science , big data , quality (philosophy) , sustainability , data mining , social science , philosophy , epistemology , sociology , ecology , biology
Deep Learning Analytics uses predictive models that provide actionable information. It is a multidisciplinary approach based on data processing, AI technology-learning enhancement, educational data mining, and visualization. The problem is that embracing DLA(Deep Learning Analytics) in evaluating data in higher education diverts educators’ attention from clearly identifying methods, benefits, and challenges of using DLA in higher education. Predictive models including random forest (RF), support vector machines (SVM), logistic regression (logistic), and Deep Learning were trained and their performances compared. The predicted value of “source of sustainability” and selected input variables were utilized to predict the drop out of learner. Expected significant outcomes and impact is that using DLA we can find the optimal learning management model for supporting services for instructors significantly impact the quality of statistics education and for learners is necessary to support announcements from instructors, for providing appropriate learning environments.