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Yoo, Mina; Jin, Sung-Hee – Educational Technology & Society, 2020
Online discussion plays an increasingly significant role in asynchronous online learning environments. While previous attempts have been made to develop learning analytics dashboards to facilitate such discussions, most of these dashboards have been designed without reference to data or visualization techniques that have been proven to make online…
Descriptors: Learning Analytics, Computer Interfaces, Visual Aids, Computer Mediated Communication
Ghilay, Yaron – Journal of Educational Technology, 2018
The aim of the study was to examine the effectiveness of a model called Video-Based Learning (VBL). VBL is designed to improve the learning of higher education courses, especially those based on activities performed on a computer screen, or learning related to the understanding of visual objects, such as formulas, equations, diagrams, etc. The…
Descriptors: Interactive Video, Higher Education, Student Attitudes, Foreign Countries
Kisanga, Dalton; Wambura, Deusdedith; Mwalongo, Finian – International Journal of Education and Development using Information and Communication Technology, 2018
Students with hearing impairment (HI) face challenges in learning including accessing educational information electronically. This study sought to assess assistive technology tools and existing e-learning user interface for students with HI in Vocational Education and Training Authority (VETA) institutions in Tanzania. The study is guided by three…
Descriptors: Assistive Technology, Electronic Learning, Computer Interfaces, Foreign Countries
Kolekar, Sucheta V.; Pai, Radhika M.; M. M., Manohara Pai – Education and Information Technologies, 2019
The term Adaptive E-learning System (AES) refers to the set of techniques and approaches that are combined together to offer online courses to the learners with the aim of providing customized resources and interfaces. Most of these systems focus on adaptive contents which are generated to the learners without considering the learning styles of…
Descriptors: Computer Interfaces, Computer Assisted Instruction, Electronic Learning, Online Courses
McManus, Margaret M.; Aiken, Robert M. – International Journal of Artificial Intelligence in Education, 2016
Our original research, to design and develop an Intelligent Collaborative Learning System (ICLS), yielded the creation of a Group Leader Tutor software system which utilizes a Collaborative Skills Network to monitor students working collaboratively in a networked environment. The Collaborative Skills Network was a conceptualization of…
Descriptors: Cooperative Learning, Artificial Intelligence, Intelligent Tutoring Systems, Sentences
Kumar, Bimal Aklesh; Chand, Sailesh – International Journal of Virtual and Personal Learning Environments, 2018
In order to assist in delivering of courses in distance mode at Fiji National University a mobile learning app was designed and evaluated. The main objective of this app was to provide learning support to learners who are studying in distance mode. The app was designed for android based smart phones and usability study was conducted to evaluate…
Descriptors: Foreign Countries, Computer Oriented Programs, Electronic Learning, Distance Education
Sözcü, Ömer Faruk; Ipek, Ismail; Kinay, Hüseyin – Universal Journal of Educational Research, 2016
The purpose of the study is to explore relationships between learners' cognitive styles of field dependence and learner variables in the preference of learner Interface design, attitudes in e-Learning instruction and experience with e-Learning in distance education. Cognitive style has historically referred to a psychological dimension…
Descriptors: Cognitive Style, Student Attitudes, Electronic Learning, Computer Interfaces
Firat, Mehmet; Sakar, A. Nurhan; Kabakci Yurdakul, Isil – Turkish Online Journal of Distance Education, 2016
One of the most important features which e-learning tools and environments must possess within the scope of lifelong learning is self-directed learning, which can be considered as a form of self-learning. The aim of this study was to determine, based on the views and recommendations of experts, interface design principles for the development of…
Descriptors: Computer Interfaces, Internet, Independent Study, Adult Students
Lkhagvasuren, Erdenesaikhan; Matsuura, Kenji; Mouri, Kousuke; Ogata, Hiroaki – International Journal of Distance Education Technologies, 2016
Mobile and ubiquitous technologies have been applied to a wide range of learning fields such as science, social science, history and language learning. Many researchers have been investigating the development of ubiquitous learning environments; nevertheless, to date, there have not been enough research works related to the reflection, analysis…
Descriptors: Electronic Learning, Educational Technology, Computer Interfaces, Learning Activities
An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Course
Baneres, David; Rodriguez-Gonzalez, M. Elena; Serra, Montse – IEEE Transactions on Learning Technologies, 2019
Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management…
Descriptors: Prediction, Feedback (Response), At Risk Students, College Freshmen
Thompson, J. Ray; Ballenger, Julia N.; Templeton, Nathan R. – International Journal of Educational Leadership Preparation, 2018
The purpose of this descriptive qualitative study was to examine the quality elements of online learning in a regional doctoral program. Utilizing the six quality dimensions of Hathaway's (2009) theory of online learning quality as a framework, the study investigated instructor-learner, learner-learner, learner-content, leaner-interface,…
Descriptors: Educational Quality, Higher Education, Online Courses, Doctoral Programs
Stoces, Michal; Masner, Jan; Jarolímek, Jan; Šimek, Pavel; Vanek, Jirí; Ulman, Miloš – International Association for Development of the Information Society, 2015
The paper discusses the development of Web educational services for specific groups. A key feature is to allow the display and use of educational materials and training services to the widest possible set of different devices, especially in the browser classic desktop computers, notebooks, tablets, mobile phones and also on different readers for…
Descriptors: Electronic Learning, Computer Interfaces, Web Based Instruction, Usability
Enriquez-Gibson, Judith – British Journal of Educational Technology, 2016
Despite the increasing focus on non-dualistic and materialist approaches in education technology studies, the materiality of the body has not been adequately examined. Because of the heavy orientation towards affordance, interaction, participation, inclusion and access at the interface or between various spatial and liminal settings, the subject's…
Descriptors: Ethnography, Educational Technology, Man Machine Systems, Human Body
Park, Hyungjoo; Song, Hae-Deok – Educational Technology & Society, 2015
Given that a user interface interacts with users, a critical factor to be considered in improving the usability of an e-learning user interface is user-friendliness. Affordances enable users to more easily approach and engage in learning tasks because they strengthen positive, activating emotions. However, most studies on affordances limit…
Descriptors: Foreign Countries, Electronic Learning, Usability, Computer Interfaces
Bull, Susan; Kay, Judy – International Journal of Artificial Intelligence in Education, 2016
The SMILI? (Student Models that Invite the Learner In) Open Learner Model Framework was created to provide a coherent picture of the many and diverse forms of Open Learner Models (OLMs). The aim was for SMILI? to provide researchers with a systematic way to describe, compare and critique OLMs. We expected it to highlight those areas where there…
Descriptors: Educational Research, Data Collection, Data Analysis, Intelligent Tutoring Systems

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