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Sönmez, Selami; Korucuk, Murat – Shanlax International Journal of Education, 2023
In this study, students' e-learning styles (ELS) and attitudes towards online learning were examined. Besides, it was also aimed to reveal the relationship between students' ELS and their attitudes towards online learning. Another of the main aims of the study is; the aim of this study is to explain the effect of university students' e-learning…
Descriptors: Educational Technology, Electronic Learning, Student Attitudes, Cognitive Style
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Ashraf, Erum; Manickam, Selvakumar; Karuppayah, Shankar; Malik, Sufiana Khatoon – Journal of Educators Online, 2023
As the drive to move from traditional face-to-face classroom learning to e-learning is ever in demand, the knowledge corpus exposed to students can be overwhelming because there is a need to automate certain functions of the e-learning framework. One of these functions is the course recommendation feature. Course recommendations help students save…
Descriptors: Electronic Learning, Cognitive Style, Student Behavior, Course Selection (Students)
Windley, Debra – Online Submission, 2023
The purpose of this participatory action research (PAR) study was to use the experiences of African American male students in an alternative learning school setting to strengthen the learning environment and support for these students. The overarching research question was: How do the insights and experiences of students in an alternative learning…
Descriptors: Nontraditional Education, High School Students, African American Students, Males
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Oliwa, Robert – Journal of Interactive Learning Research, 2022
The study examined the relationship between student satisfaction with online learning and the intelligence types postulated by Howard Gardner in the multiple intelligences theory. Student satisfaction, being a multifaceted construct, is contingent on an array of factors in an educational setting. Additionally, the introduction of an online…
Descriptors: Student Satisfaction, Multiple Intelligences, Online Courses, Electronic Learning
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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
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Ulfia Rahmi; Yulianto Santoso; Yullys Helsa; Azrul – Turkish Online Journal of Distance Education, 2025
This study aims to explore the factors that influence access and equity in blended learning in higher education because this approach may not fully support learning equity and learning access. The study utilized a correlative survey model of the quantitative research method, gathering data from 420 students from social science disciplines at state…
Descriptors: Access to Education, Equal Education, Blended Learning, Higher Education
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Yang, Tzu-Chi; Chen, Sherry Y. – Interactive Learning Environments, 2023
Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field…
Descriptors: Student Behavior, Educational Technology, Electronic Learning, Cognitive Style
Gina Tsz-Ching Lau – ProQuest LLC, 2024
The rise of virtual learning established e-learning as an emerging education delivery method (Jones et al., 2022). One of the barriers to virtual education was the lack of insight into the quality of students' online learning experience. For instance, without an active instructor to clarify the course content, virtual learners viewed instructional…
Descriptors: Online Courses, Mathematics Instruction, Electronic Learning, Educational Practices
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Xiang Wu; Huanhuan Wang; Yongting Zhang; Baowen Zou; Huaqing Hong – IEEE Transactions on Learning Technologies, 2024
Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate…
Descriptors: Artificial Intelligence, Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
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de Vasconcellos, Isabella Moreira Pereira; Robaina, Diogo Tavares; Bonanni, Carole – Journal of Education and Learning, 2020
In recent years, e-learning has been the fastest growing educational form in students' numbers, and this industry's market revenue (Lee, Choi, &Kim, 2013). Despite this growth, concern about the significantly higher student dropout rate of students in online courses as compared with conventional learning environments has increased. Brazil has…
Descriptors: Decision Making, Dropout Rate, Dropouts, Adult Students
Miller, Michael T. – Online Submission, 2021
Adult learners have more options for enrolling in postsecondary education than ever before, and they are able to use their learning style preference in deciding which program best meets their needs. For some of these students, those programs are fully online, and for others, there is minimal use of technology. As technology grows and become more…
Descriptors: Sense of Community, Adult Students, Electronic Learning, College Students
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Zeynep Tatli; Ahmet Gülay; Bahar Muradoglu; Seyma Nur Bekar – Journal of Educational Technology and Online Learning, 2023
This embedded design mixed-method study aimed to evaluate the teaching materials developed by primary school teacher candidates using Web 2.0 tools in line with their learning styles. The participants comprised 60 primary school teacher candidates identified via purposive sampling. The data were collected with the Maggie McVay Lynch Learning Style…
Descriptors: Elementary School Teachers, Instructional Materials, Electronic Learning, Online Courses
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Sarika Sharma; Jatinderkumar R. Saini – Interactive Learning Environments, 2024
During the COVID-19 pandemic period of almost two years, online teaching was adopted by Higher Educational Institutes (HEIs) mostly as an emergency measure to maintain endurance in teaching-learning activities in academics. Although a lot of research works have focussed on the teaching-learning strategies deployed during the pandemic period, the…
Descriptors: Online Courses, Electronic Learning, Cognitive Ability, Cognitive Style
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Wijaya, Adi; Setiawan, Noor Akhmad; Shapiai, Mohd Ibrahim – Electronic Journal of e-Learning, 2023
This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric…
Descriptors: Bibliometrics, Cognitive Style, Diagnostic Tests, Content Analysis
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Suryawan, I. Putu P.; Jana, Padrul; Pujawan, I. Gusti N.; Hartawan, I. Gusti N. Y.; Putri, Putu Eka W. – Pegem Journal of Education and Instruction, 2023
Critical thinking ability is a very important basic ability in learning mathematics. In the era of technological advances and digitalization, a comprehensive learning approach is needed that accommodates critical thinking skills. The controversial problem-based multimodal approach with ethnomathematics is seen as being able to improve this…
Descriptors: Foreign Countries, Ethnology, Mathematics, Controversial Issues (Course Content)
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