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[HTML][HTML] A systematic literature review of student'performance prediction using machine learning techniques
Educational Data Mining plays a critical role in advancing the learning environment by
contributing state-of-the-art methods, techniques, and applications. The recent development …
contributing state-of-the-art methods, techniques, and applications. The recent development …
On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010–2018
The aim of this paper is to survey recent research publications that use Soft Computing
methods to answer education-related problems based on the analysis of educational data …
methods to answer education-related problems based on the analysis of educational data …
Explainable AI for data-driven feedback and intelligent action recommendations to support students self-regulation
Formative feedback has long been recognised as an effective tool for student learning, and
researchers have investigated the subject for decades. However, the actual implementation …
researchers have investigated the subject for decades. However, the actual implementation …
An efficient data mining technique for assessing satisfaction level with online learning for higher education students during the COVID-19
All the educational organizations mainly aim at elevating the academic performance of
students for improving the overall quality of education. In this direction, Educational Data …
students for improving the overall quality of education. In this direction, Educational Data …
Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system
In the realm of Educational Technology, personalized learning is pivotal, yet predicting
students' learning abilities based on learning styles and ICT remains challenging. We …
students' learning abilities based on learning styles and ICT remains challenging. We …
Using Machine Learning Techniques to Predict Learner Drop‐out Rate in Higher Educational Institutions
Recently, students drop** out of school at the tertiary level without prior notice or
permission has intrigued deep concern among academic authorities, instructors, and …
permission has intrigued deep concern among academic authorities, instructors, and …
F-test feature selection in Stacking ensemble model for breast cancer prediction
R Dhanya, IR Paul, SS Akula, M Sivakumar… - Procedia Computer …, 2020 - Elsevier
Cancer data sets contains many details of patient information, out of which only a few
attributes contribute in predicting the accurate stage of cancer. Certain attributes of the entire …
attributes contribute in predicting the accurate stage of cancer. Certain attributes of the entire …
[PDF][PDF] Develo** web-based support systems for predicting poor-performing students using educational data mining techniques
The primary goal of educational systems is to enrich the quality of education by maximizing
the best results and minimizing the failure rate of poor-performing students. Early predicting …
the best results and minimizing the failure rate of poor-performing students. Early predicting …
A review of machine learning methods used for educational data
Integrating machine learning (ML) methods in educational research has the potential to
greatly impact upon research, teaching, learning and assessment by enabling personalised …
greatly impact upon research, teaching, learning and assessment by enabling personalised …
Investigating the Importance of Demographic Features for EDM-Predictions.
Demographic features are commonly used in Educational Data Mining (EDM) research to
predict at-risk students. Yet, the practice of using demographic features has to be considered …
predict at-risk students. Yet, the practice of using demographic features has to be considered …