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 …
Predicting student outcomes in online courses using machine learning techniques: A review
Recent years have witnessed an increased interest in online education, both massive open
online courses (MOOCs) and small private online courses (SPOCs). This significant interest …
online courses (MOOCs) and small private online courses (SPOCs). This significant interest …
[PDF][PDF] Fractional-Iterative BiLSTM Classifier: A Novel Approach to Predicting Student Attrition in Digital Academia
Virtual learning circumstances have been observed as consistent growth over the years. The
widespread use of online learning leads to an emerging amount of enrollments, also from …
widespread use of online learning leads to an emerging amount of enrollments, also from …
Ensemble models based on CNN and LSTM for dropout prediction in MOOC
K Talebi, Z Torabi, N Daneshpour - Expert Systems with Applications, 2024 - Elsevier
Abstract Massive Open Online Courses (MOOCs) have gained a lot of popularity recently.
Despite the large number of students enrolled in these courses, a large percentage drop out …
Despite the large number of students enrolled in these courses, a large percentage drop out …
Imbalanced classification methods for student grade prediction: a systematic literature review
Student success is essential for improving the higher education system student outcome.
One way to measure student success is by predicting students' performance based on their …
One way to measure student success is by predicting students' performance based on their …
Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs
X Xia, W Qi - International Journal of Educational Technology in …, 2023 - Springer
The temporal sequence of learning behavior is multidimensional and continuous in MOOCs.
On the one hand, it supports personalized learning methods, achieves flexible time and …
On the one hand, it supports personalized learning methods, achieves flexible time and …
Integrating LA and EDM for improving students Success in higher Education using FCN algorithm
EDM and LA are two fields that study how to use facts to get more academic learning and
enhance the students' entire performance. Both areas are concerned with a broad range of …
enhance the students' entire performance. Both areas are concerned with a broad range of …
A systematic review for MOOC dropout prediction from the perspective of machine learning
J Chen, B Fang, H Zhang, X Xue - Interactive Learning …, 2024 - Taylor & Francis
High dropout rate exists universally in massive open online courses (MOOCs) due to the
separation of teachers and learners in space and time. Dropout prediction using the …
separation of teachers and learners in space and time. Dropout prediction using the …
[HTML][HTML] A flexible feature selection approach for predicting students' academic performance in online courses
Educators' loss of ability to read students' comprehension level during the class through
quick questions or nonverbal communication is one of the main challenges of online and …
quick questions or nonverbal communication is one of the main challenges of online and …
Massive open online course adoption amongst newly graduated health care providers
A Duncan, M Premnazeer… - Advances in Health …, 2022 - Springer
Abstract Massive Open Online Courses (MOOCs) are flexible offerings that deliver content to
a large audience in a virtual platform. MOOCs are increasingly accessed by health …
a large audience in a virtual platform. MOOCs are increasingly accessed by health …