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Vui-Yee Koon – Interactive Learning Environments, 2023
Scientific research on mobile learning and its implications on humanistic education has grown tremendously. Within the context of an overview of its forerunners (i.e. educational technology) and critical characteristics concerning the humanistic perspective in education, bibliometric findings on mobile learning publications in humanistic education…
Descriptors: Journal Articles, Electronic Learning, Humanistic Education, Bibliometrics
Santos, Brian James – ProQuest LLC, 2019
This study investigated the relationship between adult learner attitudes toward technology and learning style. A sample of community college Spanish students in a Midwestern city in the United States was surveyed to determine (a) an attitude score toward Spanish e-instruction based on the Lukow ATUTS (Attitudes toward Using Technology Survey) and…
Descriptors: Adult Students, Cognitive Style, Electronic Learning, Second Language Instruction
Khamparia, Aditya; Pandey, Babita – International Journal of Web-Based Learning and Teaching Technologies, 2018
E-learning and online education has made great improvements in the recent past. It has shifted the teaching paradigm from conventional classroom learning to dynamic web based learning. Due to this, a dynamic learning material has been delivered to learners, instead ofstatic content, according to their skills, needs and preferences. In this…
Descriptors: Classification, Electronic Learning, Online Courses, Anxiety
Al-Azawei, Ahmed; Parslow, Patrick; Lundqvist, Karsten – Australasian Journal of Educational Technology, 2017
This study assesses learner perceptions of a blended e-learning system (BELS) and the feasibility of accommodating educational hypermedia systems (EHSs) according to learning styles using a modified version of the technology acceptance model (TAM). Recently, Moodle has been adopted by an Iraqi university alongside face-to-face (F2F) classrooms to…
Descriptors: Cognitive Style, Blended Learning, Computer Attitudes, Models
Pinpathomrat, Nakarin; Gilbert, Lester; Wills, Gary B. – International Association for Development of the Information Society, 2013
This research investigates the factors that affect a students' take-up and continued use of E-learning. A mathematical model was constructed by applying three grounded theories; Unified Theory of Acceptance and Use of Technology, Keller's ARCS model, and Expectancy Disconfirm Theory. The learning preference factor was included in the model.…
Descriptors: Electronic Learning, Mathematical Models, Grounded Theory, Higher Education
Schneiderheinze, Douglas D. – ProQuest LLC, 2011
Distance learning is gaining popularity in many education environments. Online classes are on the increase because students need alternatives to traditional face-to-face classroom training. Time constraint on today's students coupled with the need for education to keep up with advances in technology, in all fields, has forced educators to…
Descriptors: Cognitive Style, Computer Attitudes, Positive Attitudes, Electronic Learning
Bhrommalee, Panu – ProQuest LLC, 2011
Online learning provides learners with more convenient and flexible ways of learning than does a traditional learning environment. Many Thai universities have implemented online learning for their students despite a lack of knowledge and understanding about students' attitudes toward and behavioral intention to using the system. The purpose of…
Descriptors: Electronic Learning, Cognitive Style, Student Attitudes, Online Courses
Graff, Martin – Electronic Journal of e-Learning, 2003
The studies described in this paper sought to investigate several forms of online learning and assessment methods in terms their efficacy in facilitating student learning. The studies also sought to investigate how participants rated each method. Attitudes toward computer-assisted learning were not related to performance on each of the online…
Descriptors: Online Courses, Electronic Learning, Educational Technology, Technology Uses in Education
Contreras, Carlos L. M. – Quarterly Review of Distance Education, 2004
Demographic and personality variables and computer use were used to predict computer self-confidence with a sample of students enrolled in online college-credit classes. Computer self-confidence was measured with one 10-choice question. Demographic variables included age, annual income, geographic region, gender, and ethnicity. Computer use was…
Descriptors: Income, Age Differences, Ethnic Groups, Gender Differences