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Zulherman; Supriansyah; Desvian Bandarsyah; Mohamed Nazreen Shahul Hamid – Journal of Education and Learning (EduLearn), 2025
Online and distance learning technology with the learning management system (LMS) is an example of the application of online learning models at universities, which is the impact of technological developments. However, advances in LMS technology still need to be implemented in universities, the problem of university readiness being the main factor.…
Descriptors: Learning Management Systems, Models, Universities, Electronic Learning
Michael Wade Ashby – ProQuest LLC, 2024
Whether machine learning algorithms effectively predict college students' course outcomes using learning management system data is unknown. Identifying students who will have a poor outcome can help institutions plan future budgets and allocate resources to create interventions for underachieving students. Therefore, knowing the effectiveness of…
Descriptors: Artificial Intelligence, Algorithms, Prediction, Learning Management Systems
Nour Eddine El Fezazi; Smaili El Miloud; Ilham Oumaira; Mohamed Daoudi – Educational Process: International Journal, 2025
Background/purpose: Mobile learning (M-learning) has become a crucial component of higher education due to the increasing demand for flexible and adaptive learning environments. However, ensuring personalized and effective M-learning experiences remains a challenge. This study aims to enhance M-learning effectiveness by introducing an AI-driven…
Descriptors: Electronic Learning, Learning Management Systems, Instructional Effectiveness, Artificial Intelligence
Bulut, Okan; Gorgun, Guher; Yildirim-Erbasli, Seyma N.; Wongvorachan, Tarid; Daniels, Lia M.; Gao, Yizhu; Lai, Ka Wing; Shin, Jinnie – British Journal of Educational Technology, 2023
As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data-driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their…
Descriptors: Formative Evaluation, Learning Analytics, Models, Learning Management Systems
Kuadey, Noble Arden; Mahama, Francois; Ankora, Carlos; Bensah, Lily; Maale, Gerald Tietaa; Agbesi, Victor Kwaku; Kuadey, Anthony Mawuena; Adjei, Laurene – Interactive Technology and Smart Education, 2023
Purpose: This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach: The proposed model for this study adopted a unified theory of acceptance and use of technology…
Descriptors: Foreign Countries, College Students, Learning Management Systems, Student Behavior
Erum Ashraf; Selvakumar Manickam; Khurrum Mahmood; Shams Ul Arfeen Laghari; Amber Baig – Journal of Educators Online, 2025
The recommendation of courses in an online educational system is done using different factors that include course length, duration, and learning material information. The course design is based on the instructor's teaching style, which may not necessarily correspond with the various learning preferences of students. Matching the learning style of…
Descriptors: Online Courses, Electronic Learning, Cognitive Style, Teaching Styles
Kamolchart Klomim; Boonsong Kuayngern – Journal of Education and Learning, 2023
This research piece has the following goals: (1) to create a curriculum for creating a competency-based learning management system based on the economically motivated approach (BCG Model) by utilizing the proactive learning management concept for students practice teaching professional experience, (2) to assess the success of the curriculum in…
Descriptors: College Freshmen, Student Teaching, Competency Based Teacher Education, Curriculum Design
Md Akib Zabed Khan; Agoritsa Polyzou – Journal of Educational Data Mining, 2024
In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using…
Descriptors: Academic Advising, Courses, Data Use, Artificial Intelligence
García-Murillo, Gabriel; Novoa-Hernández, Pavel; Rodri?uez, Rocío Serrano – Interactive Learning Environments, 2023
In this study, we report on a Systematic Mapping Study (SMS) for the application of technology acceptance models to Moodle under the prism of latent variable modeling. Based on an automatic search including primary studies from journals, conferences, and book chapters during 2001 to 2019, 41 primary were selected. We aim to contribute to a better…
Descriptors: Learning Management Systems, College Students, Technology Uses in Education, Educational Research
Thuy Dung Pham Thi; Nam Tien Duong – Education and Information Technologies, 2024
With the explosive growth of various applications on the Internet, higher education institutions have advocated distance learning courses, making research on online learning increasingly important. This study attempts to emphasize the characteristics of instruction in online learning systems, using the Theory of Planned Behavior. Two groups of…
Descriptors: Electronic Learning, College Students, Behavior Theories, Intention
Hua Ma; Wen Zhao; Yuqi Tang; Peiji Huang; Haibin Zhu; Wensheng Tang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…
Descriptors: College Students, Learning Analytics, Learning Management Systems, Academic Achievement
Saba Sareminia; Vida Mohammadi Dehcheshmeh – International Journal of Information and Learning Technology, 2024
Purpose: Although E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of…
Descriptors: Educational Improvement, Electronic Learning, Personality Traits, Models
Audrey Kate Eagle – ProQuest LLC, 2024
This dissertation in practice investigated and addressed the issue of low faculty engagement with instructional design support (IDS) office support services at a regional comprehensive university in the United States. The Performance Improvement/Human Performance Technology (PI/HPT) model used in this study is a practitioner-based performance…
Descriptors: Instructional Design, Universities, College Faculty, Models
J. Bryan Osborne; Andrew S. I. D. Lang – Journal of Postsecondary Student Success, 2023
This paper describes a neural network model that can be used to detect at- risk students failing a particular course using only grade book data from a learning management system. By analyzing data extracted from the learning management system at the end of week 5, the model can predict with an accuracy of 88% whether the student will pass or fail…
Descriptors: Identification, At Risk Students, Learning Management Systems, Prediction
Olga Ovtšarenko – Discover Education, 2024
Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D…
Descriptors: Artificial Intelligence, Algorithms, Technology Uses in Education, Opportunities

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