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O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
Wang, Sufen; Du, Ming; Yu, Rong; Wang, Zhijun; Sun, Jingjing; Wang, Ling – Interactive Learning Environments, 2023
It has been controversial whether the matching of learning styles with teaching environment has improved the teaching effects. This paper constructs matching modes by choosing Sternberg's three learning styles (liberal leaning, internal scope and global level) and adopts curriculum comprehensiveness and instructing modes. The research, based on…
Descriptors: Foreign Countries, Cognitive Style, Cognitive Processes, Information Processing
Lertnattee, Verayuth; Wangwattana, Bunyapa – Interactive Learning Environments, 2021
In the academic year of 2019, the designed personalized learning and assessment was applied to the fourth-year pharmacy students who registered for the Pharmacognosy Laboratory in the Faculty of Pharmacy, Silpakorn University. We allowed all students to do the experiment as they preferred. We created a personalized assessment that allowed the…
Descriptors: Individualized Instruction, Pharmaceutical Education, Laboratory Equipment, Identification
Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification
Chen, Sherry Y.; Huang, Pei-Ren; Shih, Yu-Cheng; Chang, Li-Ping – Interactive Learning Environments, 2016
In the past decade, a number of personalized learning systems have been developed and they mainly focus on learners' prior knowledge. On the other hand, previous research suggested that gender differences and cognitive styles have great effects on student learning. To this end, this study examines how human factors, especially gender differences…
Descriptors: Prior Learning, Student Characteristics, Gender Differences, Cognitive Style
Wongwatkit, Charoenchai; Srisawasdi, Niwat; Hwang, Gwo-Jen; Panjaburee, Patcharin – Interactive Learning Environments, 2017
The advancement of computer and communication technologies has enabled students to learn across various real-world contexts with supports from the learning system. In the meantime, researchers have emphasized the necessity of providing personalized learning guidance or support by considering individual students' status and needs in order to…
Descriptors: Electronic Learning, Web Based Instruction, Educational Diagnosis, Formative Evaluation
Filippidis, Stavros K.; Tsoukalas, Ioannis A. – Interactive Learning Environments, 2009
An adaptive educational system that uses adaptive presentation is presented. In this system fragments of different images present the same content and the system can choose the one most relevant to the user based on the sequential-global dimension of Felder-Silverman's learning style theory. In order to retrieve the learning style of each student…
Descriptors: Cognitive Style, Questionnaires, Instructional Materials, Statistical Analysis