Publication Date
In 2025 | 3 |
Since 2024 | 15 |
Since 2021 (last 5 years) | 55 |
Since 2016 (last 10 years) | 57 |
Since 2006 (last 20 years) | 57 |
Descriptor
MOOCs | 57 |
Models | 57 |
Foreign Countries | 19 |
Electronic Learning | 16 |
Prediction | 14 |
Artificial Intelligence | 12 |
Student Attitudes | 11 |
Higher Education | 10 |
Learning Processes | 10 |
Teaching Methods | 10 |
Student Behavior | 9 |
More ▼ |
Source
Author
El Faddouli, Nour-eddine | 3 |
Qi, Wanxue | 2 |
Sebbaq, Hanane | 2 |
Xia, Xiaona | 2 |
Abdelhadi Raihani | 1 |
Albert, Leslie Jordan | 1 |
Alessandra Costa | 1 |
Alexandron, Giora | 1 |
Amir Reza Rahimi | 1 |
Assani Radjabu | 1 |
Azhar, Aqil Zainal | 1 |
More ▼ |
Publication Type
Education Level
Higher Education | 17 |
Postsecondary Education | 17 |
Secondary Education | 4 |
Adult Education | 1 |
Early Childhood Education | 1 |
Elementary Education | 1 |
Elementary Secondary Education | 1 |
High Schools | 1 |
Audience
Location
China | 8 |
Hong Kong | 2 |
India | 2 |
South Africa | 2 |
South Korea | 2 |
Bangladesh | 1 |
California (Stanford) | 1 |
France | 1 |
Indonesia | 1 |
Iran | 1 |
Kenya | 1 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
Test of English for… | 1 |
What Works Clearinghouse Rating
Nabila Khodeir; Fatma Elghannam – Education and Information Technologies, 2025
MOOC platforms provide a means of communication through forums, allowing learners to express their difficulties and challenges while studying various courses. Within these forums, some posts require urgent attention from instructors. Failing to respond promptly to these posts can contribute to higher dropout rates and lower course completion…
Descriptors: MOOCs, Computer Mediated Communication, Conferences (Gatherings), Models
Hmedna, Brahim; Bakki, Aicha; Mezouary, Ali El; Baz, Omar – Smart Learning Environments, 2023
Massive Open Online Courses (MOOCs) are revolutionizing online education and have become a popular teaching platform. However, traditional MOOCs often overlook learners' individual needs and preferences when designing learning materials and activities, resulting in suboptimal learning experiences. To address this issue, this paper proposes an…
Descriptors: MOOCs, Student Attitudes, Preferences, Cognitive Style
Yuan Liu; Yongquan Dong; Chan Yin; Cheng Chen; Rui Jia – Education and Information Technologies, 2024
The open online course (MOOC) platform has seen an increase in usage, and there are a growing number of courses accessible for people to select. An effective method is urgently needed to recommend personalized courses for users. Although the existing course recommendation models consider that users' interests change over time, they often model…
Descriptors: MOOCs, Online Courses, Models, Course Selection (Students)
Liang, Zibo; Mu, Lan; Chen, Jie; Xie, Qing – Education and Information Technologies, 2023
In recent years, online learning methods have gradually been accepted by more and more people. A large number of online teaching courses and other resources (MOOCs) have also followed. To attract students' interest in learning, many scholars have built recommendation systems for MOOCs. However, students need a variety of different learning…
Descriptors: MOOCs, Artificial Intelligence, Graphs, Educational Resources
Xia, Xiaona; Qi, Wanxue – Education and Information Technologies, 2023
MOOCs might be an important organization way to realize the online learning process. Online technology and sharing technology enable MOOCs to realize the adaptive scheduling of learning resources, as well as the independent construction of learning sequences. At the same time, it also generates a large number of complex learning behaviors. How to…
Descriptors: MOOCs, Learning Processes, Learning Analytics, Graphs
Heather Allmond Barker; Hollylynne S. Lee; Shaun Kellogg; Robin Anderson – Online Learning, 2024
Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify…
Descriptors: MOOCs, Motivation, Enrollment, Professional Development
Sebbaq, Hanane; El Faddouli, Nour-eddine – International Review of Research in Open and Distributed Learning, 2022
The quality assurance of MOOCs focuses on improving their pedagogical quality. However, the tools that allow reflection on and assistance regarding the pedagogical aspects of MOOCs are limited. The pedagogical classification of MOOCs is a difficult task, given the variability of MOOCs' content, structure, and designs. Pedagogical researchers have…
Descriptors: MOOCs, Classification, Educational Objectives, Models
Aïcha Bakki; Lahcen Oubahssi; Youness Laghouaouta; Sébastien George – Interactive Learning Environments, 2024
Business Process Model and Notation (BPMN) is a standard formalism for business process modeling that is very popular in professional practices due to its expressiveness, the well-defined meta-model, and its easiness of use by non-technical users. For instance, BPMN2.0 is used for business processes in commercial areas such as banks, shops,…
Descriptors: MOOCs, Learning Management Systems, Business Education, Models
Swamy, Vinitra; Radmehr, Bahar; Krco, Natasa; Marras, Mirko; Käser, Tanja – International Educational Data Mining Society, 2022
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in humancentric fields. We implement five state-of-the-art methodologies for explaining black-box machine learning models…
Descriptors: Artificial Intelligence, Academic Achievement, Grade Prediction, MOOCs
Li, Yuanmin; Chen, Dexin; Zhan, Zehui – Interactive Technology and Smart Education, 2022
Purpose: The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC) personalized recommendation method to help learners efficiently obtain MOOC resources. Design/methodology/approach: This study introduced ontology construction technology and a new semantic association algorithm…
Descriptors: MOOCs, Individualized Instruction, Models, Student Characteristics
Wang Jing Hao; Zaidatun Tasir – Journal of Information Technology Education: Research, 2024
Aim/Purpose: This study aims to develop a theoretical framework for enhancing students' higher-order thinking skills (HOTS) by integrating massive open online courses (MOOCs) with gamification elements. Background: There is a growing demand to develop students' innovative thinking abilities through MOOCs, focusing on higher-order thinking skills…
Descriptors: MOOCs, Gamification, Thinking Skills, Skill Development
Zankadi, Hajar; Idrissi, Abdellah; Daoudi, Najima; Hilal, Imane – Education and Information Technologies, 2023
Interests play an essential role in the process of learning, thereby enriching learners 'interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this…
Descriptors: Students, Social Media, Content Analysis, Interests
Xia, Xiaona; Qi, Wanxue – International Journal of Educational Technology in Higher Education, 2023
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 space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an…
Descriptors: MOOCs, Dropouts, Prediction, Decision Making
Deeva, Galina; De Smedt, Johannes; De Weerdt, Jochen – IEEE Transactions on Learning Technologies, 2022
Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by massive open online course (MOOC) providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by…
Descriptors: Electronic Learning, MOOCs, Dropouts, Prediction
Jing Chen; Bei Fang; Hao Zhang; Xia Xue – Interactive Learning Environments, 2024
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 machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…
Descriptors: MOOCs, Potential Dropouts, Prediction, Artificial Intelligence