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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
Costley, Jamie; Lange, Christopher – Interactive Learning Environments, 2023
The use of e-learning personalization allows learners to control their learning by choosing which content to process and how to process it. In order to explain the processes that occur when students use e-learning personalization, this study looks at how it interacts with two other variables: sequencing and fading, a scaffolding technique where…
Descriptors: Electronic Learning, Individualized Instruction, Cognitive Processes, Difficulty Level
Benmesbah, Ouissem; Lamia, Mahnane; Hafidi, Mohamed – Interactive Learning Environments, 2023
Adaptive learning has garnered researchers' interest. The main issue within this field is how to select appropriate learning objects (LOs) based on learners' requirements and context, and how to combine the selected LOs to form what is known as an adaptive learning path. Heuristic and metaheuristic approaches have achieved significant progress on…
Descriptors: Algorithms, Teaching Methods, Educational Innovation, Genetics
Bingxue Zhang; Yang Shi; Yuxing Li; Chengliang Chai; Longfeng Hou – Interactive Learning Environments, 2023
The adaptive learning environment provides learning support that suits individual characteristics of students, and the student model of the adaptive learning environment is the key element to promote individualized learning. This paper provides a systematic overview of the existing student models, consequently showing that the Elo rating system…
Descriptors: Electronic Learning, Models, Students, Individualized Instruction
Desheng Yan; Guangming Li – Interactive Learning Environments, 2024
Smart education, with its intelligent, individualized, and technologized content, represents people's lofty expectations for future education. It provides a good learning platform for teaching and an important environment in which students' digital learning power can be developed in the context of the information technology era. Digital learning…
Descriptors: Electronic Learning, Information Technology, Artificial Intelligence, Educational Environment
Yousef, Ahmed Mohamed Fahmy; Khatiry, Ahmed Ramadan – Interactive Learning Environments, 2023
Several governments across the world have temporarily closed educational institutions due to the COVID-19 pandemic. In response, numerous universities have seen a growing trend towards online learning scenarios. Thus, learning takes place not just within a person, but within and across the networks. However, the current implementations of open…
Descriptors: Learning Analytics, Individualized Instruction, Reflection, Learning Processes
Baginda Anggun Nan Cenka; Harry B. Santoso; Kasiyah Junus – Interactive Learning Environments, 2023
Presently, learning is more flexible, personal and has richer learning resources. In the digital era, students use digital tools in almost all aspects of learning, such as seeking information, note-taking, discussion and communication, which is in line with personal learning environments. Therefore, this study proposes a conceptual model of the…
Descriptors: Educational Environment, Lifelong Learning, Educational Resources, Electronic Learning
Liu, Na; Pu, Quanlin – Interactive Learning Environments, 2023
One-to-one online learning has become pervasive in distance education. However, factors affecting learners' continuance intention toward one-to-one online learning are not well known. This study proposed a model to explain learners' continuance intention toward one-to-one online learning. The model extends previous technology acceptance models and…
Descriptors: Intention, Individualized Instruction, Electronic Learning, Distance Education
Zhang, Lishan; Pan, Mengqi; Yu, Shengquan; Chen, Ling; Zhang, Jing – Interactive Learning Environments, 2023
This paper introduces a system that supports student-centered online one-to-one tutoring and evaluates the practical value of the system by running an experiment with 64 experienced mathematics teachers and 810 students in Grade 7. The experiment lasted for 50 days. A comprehensive evaluation was performed using students' academic performance…
Descriptors: Mathematics Teachers, Grade 7, Middle School Students, Middle School Teachers
Xu, Xiaoshu; Zhu, Xiaoshen; Chan, Fai Man – Interactive Learning Environments, 2023
Personal Learning Environment (PLE) represents a shift of learning paradigm towards learner-centered pedagogy, where users become masters of their own learning. PLEs are best used by learners with Self-Regulated Learning (SRL) abilities. Previous research showed that learners felt lost or socially isolated in PLEs due to their limited SRL…
Descriptors: Educational Environment, Individualized Instruction, Pilot Projects, College Students
Li, Kam Cheong; Wong, Billy Tak-Ming – Interactive Learning Environments, 2021
This paper provides a comprehensive review of the features and trends of personalised learning. The review covers a total of 203 journal articles collected from Scopus, which were published from 2001 to 2018 and involved personalised learning practices. Comparing the practices between 2001-2009 and 2010-2018, there was a clear trend that they…
Descriptors: Individualized Instruction, Educational Trends, Literature Reviews, Technology Uses in Education
Yuan, Chia-Ching; Li, Cheng-Hsuan; Peng, Chin-Cheng – Interactive Learning Environments, 2023
Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization…
Descriptors: Artificial Intelligence, Man Machine Systems, Technology Uses in Education, Educational Technology
Zou, Di; Wang, Minhong; Xie, Haoran; Cheng, Gary; Wang, Fu Lee; Lee, Lap-Kei – Interactive Learning Environments, 2021
Personalized learning has become an important and powerful paradigm catering for various needs, styles, preferences, and modes of learning. Several methods including task recommendations and path planning have recently emerged to effectively implement personalized learning using e-learning systems. The literature shows that the use of task…
Descriptors: Linguistic Theory, Vocabulary Development, Second Language Learning, Second Language Instruction
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
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
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