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Article
Chemistry, Multidisciplinary
Warunya Wunnasri et al.
Summary: MOOCs, or Massive Open Online Courses, are frequently used by students for online learning, but the success rate of such learning is often low. Machine learning techniques can be utilized to predict the success of students' learning in MOOCs, enabling the implementation of various approaches to enhance learning, such as identifying low-performing students and grouping them together. This study proposes a two-phase ensemble-based method that outperforms existing machine learning algorithms in predicting learners' grades, using features computed based on distance from the class's center and optimizing parameters through Bayesian optimization.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Feiyue Qiu et al.
Summary: E-learning, the integration of modern education and information technology, plays a significant role in promoting educational equity. To ensure the quality of e-learning, a behavior classification-based e-learning performance prediction framework and a process-behavior classification model are proposed, which are proven effective through experiments.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Multidisciplinary
Yahia Baashar et al.
Summary: The study aims to predict the academic performance of postgraduate students using different machine learning algorithms, with the artificial neural network model showing the best performance of 89% variation in CGPA. Future research should focus on predicting the academic performance of more postgraduates using different predictive variables and AI models.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Multidisciplinary Sciences
Muhammad Bilal et al.
Summary: Educational Data Mining is widely used to predict student performance, but it's challenging due to the various features that may affect students' performance. This study aimed to predict the final semester results of Doctor of Veterinary Medicine students based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data was addressed through synthetic minority oversampling technique, and the Support Vector Machine model achieved the highest accuracy. The key features affecting students' performance were identified, providing useful information for predicting performance and improving achievement in academic institutes.
SCIENTIFIC REPORTS
(2022)
Review
Chemistry, Multidisciplinary
Abdallah Namoun et al.
Summary: The survey analyzed the use of intelligent models in predicting student performance in education, highlighting student online learning activities, term assessment grades, and student academic emotions as significant predictors. The study emphasizes that student learning outcomes are a crucial measure of academic success, with suggestions for future research directions.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Luca Cagliero et al.
Summary: The Learning Analytics community has been focusing on early predicting learners' performance recently. While machine learning and data mining solutions have been proposed, the best performing models often lack interpretability. This paper introduces an Explainable Learning Analytics solution to analyze learner-generated data for early success rate prediction.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Talha Mahboob Alam et al.
Summary: The disparities in education access globally are mainly due to regional differences and resource allocation. It is important to evaluate the performance of educational institutions and address the challenges in education worldwide.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Ansar Siddique et al.
Summary: In recent years, educational data mining has gained attention to improve the quality of education, with a focus on predicting student academic performance. This research specifically looked at predicting performance at the secondary level and identified critical factors influencing student outcomes. By fusing single and ensemble-based classifiers, an efficient model was developed to achieve high accuracy in predicting academic performance.
APPLIED SCIENCES-BASEL
(2021)
Review
Education & Educational Research
Balqis Albreiki et al.
Summary: Educational Data Mining plays a critical role in advancing the learning environment by providing valuable tools for understanding and utilizing student learning environments. Universities face challenges in analyzing performance, providing high-quality education, evaluating student performance, and implementing student intervention plans. The use of machine learning techniques can help predict students at risk and dropout rates.
EDUCATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Siti Dianah Abdul Bujang et al.
Summary: This study conducted a comprehensive analysis of machine learning techniques, compared the accuracy performance of different techniques for predicting student grades in the first semester, and proposed a multi-class prediction model. The results showed that the model achieved significant performance improvement when dealing with imbalanced datasets, providing a more reliable model for student grade prediction.
Review
Education & Educational Research
Anupam Khan et al.
Summary: Student performance modelling is a challenging and popular research topic in educational data mining, with multiple factors influencing performance in non-linear ways. Limited specific surveys on student performance analysis and prediction are available, primarily focusing on identifying possible predictors or modeling student performance, but lacking consideration of temporal aspects. This paper presents a systematic review of EDM studies on student performance in classroom learning, focusing on predictors, identification methods, time, and aim of prediction, being the first survey to specifically consider only classroom learning and address temporal aspects.
EDUCATION AND INFORMATION TECHNOLOGIES
(2021)
Article
Chemistry, Multidisciplinary
Raza Hasan et al.
APPLIED SCIENCES-BASEL
(2020)
Article
Computer Science, Interdisciplinary Applications
Cheng Yan et al.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2019)
Article
Computer Science, Interdisciplinary Applications
Agoritsa Polyzou et al.
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
(2019)
Editorial Material
Computer Science, Interdisciplinary Applications
Cristobal Romero et al.
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Boran Sekeroglu et al.
PROCEEDINGS OF 2019 8TH INTERNATIONAL CONFERENCE ON EDUCATIONAL AND INFORMATION TECHNOLOGY (ICEIT 2019)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Arto Hellas et al.
ITICSE 2018 COMPANION: PROCEEDINGS COMPANION OF THE 23RD ANNUAL ACM CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION
(2018)
Article
Engineering, Electrical & Electronic
Jie Xu et al.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2017)
Article
Computer Science, Information Systems
Amin Zollanvari et al.
Article
Engineering, Electrical & Electronic
Yannick Meier et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2016)
