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Mengning Mu; Man Yuan – Interactive Learning Environments, 2024
The necessity for students to clarify their own cognitive structure and the amount of their knowledge mastery for self-reflection is often ignored in building the student model in the adaptive model, which makes the construction of the cognitive structure pointless. Simultaneously, knowledge forgetting causes students' knowledge level to fall…
Descriptors: Individualized Instruction, Cognitive Processes, Graphs, Cognitive Structures
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
Mohammed Estaiteyeh; Isha DeCoito – Interactive Learning Environments, 2024
Differentiated instruction (DI) is a teaching approach that aims to achieve learning for diverse students. This study reports on promoting STEM teacher candidates' (TCs') implementation of technology-enhanced DI in teacher education courses. The research questions are: (1) How do TCs develop digital video games (DVGs) to be inclusive of DI?, and…
Descriptors: STEM Education, Preservice Teachers, Individualized Instruction, Student Needs
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
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
Li Jin; Dawei Shang – Interactive Learning Environments, 2024
Massive open online courses (MOOC) have become important in the learning process and have been adopted in higher education, especially during the COVID-19 pandemic. However, few studies investigated MOOC continuance intention (CI) for arts disciplines. Thus, an integrated framework was proposed based on the expectation-confirmation model (ECM) and…
Descriptors: Art Education, MOOCs, Computer System Design, Continuing Education
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
Sherry Y. Chen; Chia-Yi Tseng; Chao-Yang Cheng – Interactive Learning Environments, 2023
This study proposed a three-tier test to help students learn English grammar. To reduce students' anxiety, game-based learning was incorporated into the three-tier test, where personalization was also implemented to accommodate students' different needs. More specifically, we developed a Personalized Entertaining Three-Tier Test (PET3), which…
Descriptors: English (Second Language), Language Tests, Grammar, Game Based Learning
Fiedler, Sebastian H. D.; Väljataga, Terje – Interactive Learning Environments, 2020
This paper argues for conceptualizing the notion of personal learning environments in higher education from an explicit adult education perspective that emphasizes the realization, re-instrumentation, and integration of learning activity in the wider context of adult life. It discusses and re-interprets an existing proposal for modeling "the…
Descriptors: Individualized Instruction, Adult Students, Adult Education, Higher Education
Hsu, Yuling – Interactive Learning Environments, 2023
Computer simulations have become widely available to support geometric learning; however, the type and amount of guidance, as well as the freedom to engage in self-directed learning that should be allowed for children from differential socio-cultural backgrounds, have not yet been clarified. On the basis of the cognitive theory of multimedia…
Descriptors: Elementary School Students, Grade 5, Geometric Concepts, Computer Mediated Communication
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
Wang, Shuai; Christensen, Claire; Cui, Wei; Tong, Richard; Yarnall, Louise; Shear, Linda; Feng, Mingyu – Interactive Learning Environments, 2023
Adaptive learning systems personalize instruction to students' individual learning needs and abilities. Such systems have shown positive impacts on learning. Many schools in the United States have adopted adaptive learning systems, and the rate of adoption in China is accelerating, reaching almost 2 million unique users for one product alone in…
Descriptors: Comparative Analysis, Teaching Methods, Intelligent Tutoring Systems, Foreign Countries
Zhang, Jia-Hua; Zou, Liu-cong; Miao, Jia-jia; Zhang, Ye-Xing; Hwang, Gwo-Jen; Zhu, Yue – Interactive Learning Environments, 2020
Extensive studies have been conducted to diagnose and predict students' academic performance by analyzing a large amount of data related to their learning behaviors in a blended learning environment. But there is a lack of research examining how individualized learning interventions could improve students' academic performance in such a learning…
Descriptors: Individualized Instruction, Academic Achievement, Interaction, Blended Learning
Chaloupský, David; Chaloupská, Pavlína; Hrušová, Dagmar – Interactive Learning Environments, 2021
The research focuses on learning methods that address individual differences, motivation and training goals in fitness running. The aim was to examine the possibilities of use of fitness trackers in smart phones for fitness running lessons. The core of the study was to design and implement an innovative blended learning model to individualize the…
Descriptors: Foreign Countries, Physical Education, Health Related Fitness, Measurement Equipment
Xu, Xiaoshu; Chan, Fai Man; Yilin, Sun – Interactive Learning Environments, 2020
English for Specific Purposes (ESP) teacher development has become a crucial issue in ESP teaching and research. How to equip ESP teachers with necessary knowledge and skills in the digital era becomes a hot topic. A Personal Learning Environment (PLE) is a potential pedagogical approach to realize learning-centered in ESP teacher development.…
Descriptors: Educational Environment, Individualized Instruction, English for Special Purposes, Language Teachers
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