Student retention using educational data mining and predictive analytics: a systematic literature review
Student retention is an essential measurement metric in education, indicated by retention
rates, which are accumulated as students re-enroll from one academic year to the next. High …
rates, which are accumulated as students re-enroll from one academic year to the next. High …
Managing the strategic transformation of higher education through artificial intelligence
Considering the rapid advancements in artificial intelligence (AI) and their potential
implications for the higher education sector, this article seeks to critically evaluate the …
implications for the higher education sector, this article seeks to critically evaluate the …
Predicting students' performance employing educational data mining techniques, machine learning, and learning analytics
A Alam, A Mohanty - International Conference on Communication …, 2022 - Springer
Student success is important in colleges and universities since it is often used as a measure
of the institution's effectiveness. Identifying at-risk students early on and implementing …
of the institution's effectiveness. Identifying at-risk students early on and implementing …
Technology roadmap** for the e-commerce sector: A text-mining approach
Due to rapid technological advancements and shifting consumer dynamics, the e-commerce
industry has undergone rapid transformation. Further, emerging technologies, including …
industry has undergone rapid transformation. Further, emerging technologies, including …
Educational data mining and learning analytics: A review of educational management in e-learning
Purpose The purpose of this study is to identify the main perspectives and trends in
educational data mining (EDM) in the e-learning environment from a managerial …
educational data mining (EDM) in the e-learning environment from a managerial …
[HTML][HTML] Real-time prediction of science student learning outcomes using machine learning classification of hemodynamics during virtual reality and online learning …
Current data sources used for the prediction of student outcomes average about 55%
accuracy and require a significant amount of input data and time for researchers and …
accuracy and require a significant amount of input data and time for researchers and …
Educational data mining: A bibliometric analysis of an emerging field
We are now able to collect enormous amounts of information at the learner level. Mining
educational data to provide data-driven analytics has spurred great interest among …
educational data to provide data-driven analytics has spurred great interest among …
Reviewing the differences between learning analytics and educational data mining: Towards educational data science
Over the last decade, Educational Data Mining (EDM) and Learning Analytics (LA) have
evolved enormously as interrelated research areas and disciplines. Many researchers …
evolved enormously as interrelated research areas and disciplines. Many researchers …
Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review
Teacher evaluation is presented as an object of study of great interest, where multiple efforts
converge to establish models from the association of heterogeneous data from academic …
converge to establish models from the association of heterogeneous data from academic …
Learning analytics on the african continent: An emerging research focus and practice
P Prinsloo, R Kaliisa - Journal of Learning Analytics, 2022 - learning-analytics.info
While learning analytics (LA) has been highlighted as a field aiming to address systemic
equity and quality issues within educational systems between and within regions, to date, its …
equity and quality issues within educational systems between and within regions, to date, its …