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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
El Aissaoui, Ouafae; El Alami El Madani, Yasser; Oughdir, Lahcen; El Allioui, Youssouf – Education and Information Technologies, 2019
Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of…
Descriptors: Classification, Cognitive Style, Models, Electronic Learning
Rawat, Bhupesh; Dwivedi, Sanjay K. – International Journal of Information and Communication Technology Education, 2019
With the emergence of the web, traditional learning has changed significantly. Hence, a huge number of 'e-learning systems' with the advantages of time and space have been created. Currently, many e-learning systems are being used by a large number of academic institutions worldwide which allow different users of the system to perform various…
Descriptors: Electronic Learning, Student Characteristics, Learning Processes, Management Systems
Sabitha, A. Sai; Mehrotra, Deepti; Bansal, Abhay – Education and Information Technologies, 2017
Currently the challenges in e-Learning are converging the learning content from various sources and managing them within e-learning practices. Data mining learning algorithms can be used and the contents can be converged based on the Metadata of the objects. Ensemble methods use multiple learning algorithms and it can be used to converge the…
Descriptors: Electronic Learning, Metadata, Computer System Design, Design Preferences
Hew, Khe Foon; Qiao, Chen; Tang, Ying – International Review of Research in Open and Distributed Learning, 2018
Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884…
Descriptors: Learner Engagement, Large Group Instruction, Online Courses, Man Machine Systems
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
Maaliw, Renato R., III – Online Submission, 2016
Virtual Learning Environment (VLE) such as Moodle, Blackboard, and WebCT are commonly and successfully used in E-education. While they focus on supporting educators in creating and holding online courses, they typically do not consider the individual differences of learners. However, learners have different needs and characteristics such as prior…
Descriptors: Virtual Classrooms, Electronic Learning, Integrated Learning Systems, Cognitive Style