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Zhou, Yizhuo; Zhao, Jin; Zhang, Jianjun – Interactive Learning Environments, 2023
On e-learning platforms, most e-learners didn't complete the course successfully. It means that reducing dropout is a critical problem for the sustainability of e-learning. This paper aims to establish a predictive model to describe e-learners' dropout behavior, which can help the commercial e-learning platforms to make appropriate interventions…
Descriptors: Electronic Learning, Prediction, Dropouts, Student Behavior
Xinyu Li; Yizhou Fan; Tongguang Li; Mladen Rakovic; Shaveen Singh; Joep van der Graaf; Lyn Lim; Johanna Moore; Inge Molenaar; Maria Bannert; Dragan Gaševic – Journal of Learning Analytics, 2025
The focus of education is increasingly on learners' ability to regulate their own learning within technology-enhanced learning environments. Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to productively self-regulate their learning, as they typically need to…
Descriptors: Learning Analytics, Metacognition, Independent Study, Skill Development
Marc Burchart; Joerg M. Haake – IEEE Transactions on Learning Technologies, 2024
In distance education courses with a large number of students and groups, the organization and facilitation of collaborative writing tasks are challenging. Teachers need support for planning, specification, execution, monitoring, and evaluation of collaborative writing tasks in their course. This requires a collaborative learning platform for…
Descriptors: Writing Instruction, Distance Education, Large Group Instruction, 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
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
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
Hongyu Xie; He Xiao; Yu Hao – International Journal of Web-Based Learning and Teaching Technologies, 2024
Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing…
Descriptors: Artificial Intelligence, Electronic Learning, Individualized Instruction, Models
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
Kam Moi Lee; Megan Mcfarland; Kari Goin Kono – Issues and Trends in Learning Technologies, 2023
One way to achieve equitable design is to directly include users who will be impacted the most in the planning and facilitation of a project. Common financial, logistical, and/or temporal constraints reveal that direct inclusion of the people most impacted is not always possible. If this barrier arises, one promising alternative is the creation…
Descriptors: Design, Electronic Learning, Teacher Characteristics, Faculty Development
Abubaker Abdulkarim Alhitty; Tara Fryad Henari – Technology in Language Teaching & Learning, 2023
This study aims to adapt and extend the DeLone and McLean Information System (IS) success model in the context of an online English as a Foreign Language (EFL) higher education foundation program. The objective is to enhance teaching, learning, and knowledge-sharing experiences within a virtual community of practice (vCoP). The research involved…
Descriptors: Higher Education, Second Language Instruction, Second Language Learning, English (Second Language)
Patience Atukunda; Simon Peter Khabusi; John Othieno – Discover Education, 2024
This study investigates user satisfaction with e-learning systems in higher education institutions, examining the perspectives of students, lecturers, and elearning officers and heads of the department of Information Technology as key informants. A total of 375 student respondents from Diploma, Bachelor, and Masters levels, 51 lecturers, and 15…
Descriptors: Electronic Learning, Access to Education, Usability, Internet
Varun Mandalapu – ProQuest LLC, 2021
Educational data mining focuses on exploring increasingly large-scale data from educational settings, such as Learning Management Systems (LMS), and developing computational methods to understand students' behaviors and learning settings better. There has been a multitude of research dedicated to studying the student learning process, leading to…
Descriptors: Models, Student Behavior, Learning Management Systems, Data Use
Uto, Masaki; Nguyen, Duc-Thien; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2020
With the wide spread large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure the learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner's assessment workload. However, in such cases, the peer assessment…
Descriptors: Item Response Theory, Electronic Learning, Peer Evaluation, Accuracy
Tamara Lynn; Shantel Farnan; Jessica A. Rueter; Adam Moore – Journal of Special Education Preparation, 2022
Small special education programs (SSEPs) are composed of limited faculty tasked with educating interns dispersed across large geographical areas (Reid, 1994). These needs underscore a call for more flexible educational program options. Moreover, Kebritchi et al. (2017) found professors in higher education institutions sought a variety of…
Descriptors: Field Experience Programs, Models, Coaching (Performance), Feedback (Response)
Tawafak, Ragad M.; Romli, Awanis BT; Alsinani, Maryam – Education and Information Technologies, 2019
This paper focused on the improvement of student's assessment feedback and learning satisfaction in the higher education institutions in Oman using the E-learning system of University Communication Model (UCOM). During the study, an E-learning model was conceptualized using coursework program instruction, testing academic performance, faculty…
Descriptors: Feedback (Response), Electronic Learning, Foreign Countries, Universities