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Hsu, Yu-Chang; Ching, Yu-Hui – Canadian Journal of Learning and Technology, 2015
Mobile learning has become increasingly popular in the past decade due to the unprecedented technological affordances achieved through the advancement of mobile computing, which makes ubiquitous and situated learning possible. At the same time, there have been research and implementation projects whose efforts centered on developing mobile…
Descriptors: Electronic Learning, Instructional Design, Classification, Models
Yalcinalp, Serpil; Gulbahar, Yasemin – British Journal of Educational Technology, 2010
Recent developments and new directions in education have emphasised learners' needs, profile and pedagogical aspects by focusing on learner-centered approaches in educational settings. e-Learning, on the other hand, guarantees learners the opportunity of learning in their own way, and leads to new considerations in course design. e-Learning is…
Descriptors: Electronic Learning, Educational Change, Internet, Classification
Higgins, Steven E.; Mercier, Emma; Burd, Elizabeth; Hatch, Andrew – International Journal of Computer-Supported Collaborative Learning, 2011
This article reviews the research and evidence about multi-touch tables to provide an analysis of their key design features and capabilities and how these might relate to their use in educational settings to support collaborative learning. A typology of design features is proposed as a synthesis of the hardware and physical characteristics of the…
Descriptors: Evidence, Educational Research, Classification, Design Requirements
Speece, Mark – Online Submission, 2012
Adaptation to customer needs is a key component of competitiveness in any service industry. In online HE (higher education), which is increasingly worldwide, this adaptation must include consideration of learning styles. Most research shows that learning style has little impact on learning outcomes in online education. Nevertheless, students with…
Descriptors: Cognitive Style, Learning Modalities, Cultural Context, Classification
Deliyska, Boryana; Manoilov, Peter – International Journal of Distance Education Technologies, 2010
The intelligent learning systems provide direct customized instruction to the learners without the intervention of human tutors on the basis of Semantic Web resources. Principal roles use ontologies as instruments for modeling learning processes, learners, learning disciplines and resources. This paper examines the variety, relationships, and…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Curriculum Development, Lesson Plans
Magnisalis, I.; Demetriadis, S.; Karakostas, A. – IEEE Transactions on Learning Technologies, 2011
This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence…
Descriptors: Evidence, Instructional Design, Bibliographic Databases, Classification
Lavoue, Elise; George, Sebastien; Prevot, Patrick – Behaviour & Information Technology, 2012
In this article, we present a co-adaptive design approach named TE-Cap (Tutoring Experience Capitalisation) that we applied for the development of an assistance environment for tutors. Since tasks assigned to tutors in educational contexts are not well defined, we are developing an environment which responds to needs which are not precisely…
Descriptors: Foreign Countries, Tutors, Tutoring, College Faculty
Ozpolat, Ebru; Akar, Gozde B. – Computers & Education, 2009
A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user's browsing…
Descriptors: Cognitive Style, Classification, Measures (Individuals), Measurement Techniques
Pontes, Elvis, Ed.; Silva, Anderson, Ed.; Guelfi, Adilson, Ed.; Kofuji, Sergio Takeo, Ed. – InTech, 2012
With the resources provided by communication technologies, E-learning has been employed in multiple universities, as well as in wide range of training centers and schools. This book presents a structured collection of chapters, dealing with the subject and stressing the importance of E-learning. It shows the evolution of E-learning, with…
Descriptors: Foreign Countries, Educational Technology, Virtual Classrooms, Program Effectiveness
Padiotis, Ioannis; Mikropoulos, Tassos A. – Educational Technology & Society, 2010
The present research investigates the contribution of an interactive educational virtual environment on milk pasteurization to the learning outcomes of 40 students in a technical secondary school using SOLO taxonomy. After the interaction with the virtual environment the majority of the students moved to higher hierarchical levels of understanding…
Descriptors: Foreign Countries, Science Instruction, Technology Education, Misconceptions
Specht, Marcus; Burgos, Daniel – Journal of Interactive Media in Education, 2007
The paper describes a classification system for adaptive methods developed in the area of adaptive educational hypermedia based on four dimensions: What components of the educational system are adapted? To what features of the user and the current context does the system adapt? Why does the system adapt? How does the system get the necessary…
Descriptors: Hypermedia, Educational Methods, Classification, Models
Chang, Wen-Chih; Yang, Hsuan-Che; Shih, Timothy K.; Chao, Louis R. – International Journal of Distance Education Technologies, 2009
E-learning provides a convenient and efficient way for learning. Formative assessment not only guides student in instruction and learning, diagnose skill or knowledge gaps, but also measures progress and evaluation. An efficient and convenient e-learning formative assessment system is the key character for e-learning. However, most e-learning…
Descriptors: Electronic Learning, Student Evaluation, Formative Evaluation, Educational Objectives
Alexander, Bryan – Theory Into Practice, 2008
Students are, increasingly, digital content producers, and participate extensively in evolving online social networks. The emergence of the former represents subtle changes in students' experience of images, audience, copyright, ownership of learning, and technology. Experiencing the latter places students in an awkward position in terms of…
Descriptors: Intellectual Property, Educational Technology, Visual Environment, Social Networks
Spector, J. Michael; And Others – 1992
Many researchers are attempting to develop automated instructional development systems to guide subject matter experts through the lengthy and difficult process of courseware development. Because the targeted users often lack instructional design expertise, a great deal of emphasis has been placed on the use of artificial intelligence (AI) to…
Descriptors: Artificial Intelligence, Authoring Aids (Programing), Classification, Computer Assisted Instruction
Botturi, Luca – Australasian Journal of Educational Technology, 2004
This paper introduces the Quail Model, a device for the classification and visualisation of learning goals. The model is a communication tool that can smoothen the discussion within a course design team, support shared understanding, and improve decision making. Its theoretical background mingles contributions from instructional design (Bloom,…
Descriptors: Instructional Design, Models, Classification, Visualization
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