13,237 research outputs found
Personalised trails and learner profiling within e-learning environments
This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails
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Mining learning preferences in web-based instruction: Holists vs. Serialists
Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction programs. However, the majority of previous studies focus on Field Dependence/Independence. Pask's Holist/Serialist dimension has conceptual links with Field Dependence/Independence but it is left mostly unstudied. Therefore, this study focuses on identifying how this dimension of cognitive style affects learner preferences of Web-based instruction programs. A data mining approach is used to illustrate the difference in preferences between Holists and Serialists. The findings show that there are clear differences in regard to content presentation and navigation support. A set of design features were then produced to help designers incorporate cognitive styles into the development of Web-based instruction programs to ensure that they can accommodate learners' different preferences.This work is partially funded by National Science Council, Taiwan, ROC (NSC 98-2511-S-008-012- MY3; NSC 99-
2511-S-008 -003 -MY2; NSC 99-2631-S-008-001)
Information systems for interactive learning: Design perspective
This paper aims to present and discuss educational issues and relevant research to universities and colleges in the Arabian Gulf Region. This include cultural, studentsâ learning preferences and the use of information and communication technology. It particularly focuses on interactive learning through the consideration of learning styles. It explores the sequential-global learning styles profile of undergraduate students as part of a continuous research in Information Systems design with a particular focus on the design of Interactive Learning Systems (ILSs). A study to examine the learning style profile of undergraduate students in a cohort of Management Information Systems at a UAE university has been conducted, and a discussion and recommendations on how these findings can be reflected on the design of ILSs are provided
ADAPTING mLEARNING ENVIRONMENTS ON LEARNERSâ COGNITIVE STYLES AND VISUAL WORKING MEMORY SPAN
The research that is described in this paper focuses on incorporating theories of individual differences in information processing within the context of mobile hypertext and hypermedia interactive environments. Based on previous findings of the authors in the field of adaptive eLearning, the main purpose was to enhance the quality of information presentation and usersâ interactions in the Web by matching their specific needs and preferences. Our more recent experiments, explore how to improve learning process by adapting course content presentation to student cognitive styles and capabilities in mobile environments such as PDA phones. A framework has been developed to comprehensively model studentâs cognitive styles and visual working memory span and present the appropriate subject matter, including the content, format, guidance, etc. to suit an individual student by increasing efficiency during interaction. Main aim is to overcome constraints like small screen size and processing/memory capabilities for navigation enhancements that limit the presentation and guidance of the material. An increase on usersâ satisfaction as well as more efficient information processing (both in terms of accuracy and task completion time), has been observed in the personalized condition than the original one. Consequently, it is supported that human factors may be used in order to enhance the design of mobile hypertext (or hypermedia) environments in a measurable and meaningful way
Adaptive courseware design based on learner character
During last decade, a great advance has been done in both theoretical research and
software construction of Adaptive Hypermedia Systems (AHS). The article discusses
the practical approach taken for authoring and instructional design of adaptive
courseware based on learner character, namely learner goals and preferences,
learner style, and learner performance and satisfaction level. This approach is
adopted at pilot test of ADOPTA - adaptive technology-enhanced platform for
edutainment. The authoring process relies strongly on an enhanced learning object
metadata support, where learning styles are used for adaptive navigation within the
narrative storyboard graph. On other side, both learner knowledge and satisfaction
level determine adaptive content selection
Personalised trails and learner profiling in an e-learning environment
This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails
E-Learning and Intelligent Planning: Improving Content Personalization
Combining learning objects is a challenging topic
because of its direct application to curriculum generation,
tailored to the students' profiles and preferences. Intelligent
planning allows us to adapt learning routes (i.e. sequences of
learning objects), thus highly improving the personalization of
contents, the pedagogical requirements and specific necessities of
each student.
This paper presents a general and effective approach to
extract metadata information from the e-learning contents, a
form of reusable learning objects, to generate a planning domain
in a simple, automated way. Such a domain is used by an
intelligent planner that provides an integrated recommendation
system, which adapts, stores and reuses the best learning routes
according to the students' profiles and course objectives. If any
inconsistency happens during the route execution, e.g. the student
fails to pass an assessment test which prevents him/her from
continuing the natural course of the route, the systeGarrido, A.; Morales, L. (2014). E-Learning and Intelligent Planning: Improving Content Personalization. IEEE Revista Iberoamericana de TecnologĂas del Aprendizaje. 9(1):1-7. doi:10.1109/RITA.2014.2301886S179
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