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Competency Levels and Influential Factors of College Students' Mobile Learning Readiness in Thailand
Diteeyont, Watsatree; Heng-Yu, Ku – Smart Learning Environments, 2023
One of the key successes of learning through mobile technology comes from the competencies of learners. This study aimed to investigate the overall competency levels of mobile learning readiness and four influential factors (connectivist learners, technology readiness, self-directed learning, and netiquette) that may impact college students'…
Descriptors: Telecommunications, Handheld Devices, Educational Technology, Readiness
Feng, Zeping; Lau, Newman; Zhu, Mengxiao; Liu, Mengru; Refati, Rehe; Huang, Xiao; Lee, Kun-pyo – Smart Learning Environments, 2023
In Mainland China, the sports training process of most players is highly homogenized, the convergence of which makes them ineffectively be identified with their individual and specific profile and difficult for them to play the sports according to their strengths and characteristics. Moreover, existing sports training software does not…
Descriptors: Foreign Countries, Youth, Team Sports, Training
Steffen Steinert; Karina E. Avila; Stefan Ruzika; Jochen Kuhn; Stefan Küchemann – Smart Learning Environments, 2024
Effectively supporting students in mastering all facets of self-regulated learning is a central aim of teachers and educational researchers. Prior research could demonstrate that formative feedback is an effective way to support students during self-regulated learning. In this light, we propose the application of Large Language Models (LLMs) to…
Descriptors: Formative Evaluation, Feedback (Response), Natural Language Processing, Artificial Intelligence
Ahmady, Soleiman; Kohan, Noushin; Mirmoghtadaie, Zohreh Sadat; Hamidi, Hadi; Sabet Divshali, Babak; Rakhshani, Tayebeh; Khani Jeihooni, Ali – Smart Learning Environments, 2023
Background: Today, methods that enable students to benefit from online programs to the fullest and learn independently and self-directed are of critical importance. Many scales have been developed to measure self-directed learning in the physical classroom. This study was conducted to design and assess the psychometric properties of an instrument…
Descriptors: Test Construction, Psychometrics, Educational Technology, Test Validity
Islam, Monjurul; Mazlan, Nurul Hijja; Al Murshidi, Ghadah; Hoque, Mohammed Shamsul; Karthiga, S. V.; Reza, Mohoshin – Smart Learning Environments, 2023
Virtual Classroom (VC) learning approaches have recently drawn considerable attention because they have the potential to encourage student engagement to ensure active and collaborative learning. Although research on online learning has gained visibility in recent times, VC learning has not received notable attention, especially in Gulf countries…
Descriptors: Virtual Classrooms, Electronic Learning, COVID-19, Pandemics
Oliveira, Wilk; Hamari, Juho; Joaquim, Sivaldo; Toda, Armando M.; Palomino, Paula T.; Vassileva, Julita; Isotani, Seiji – Smart Learning Environments, 2022
Gamification refers to the attempt to transform different kinds of systems to be able to better invoke positive experiences such as the flow state. However, the ability of such intervention to invoke flow state is commonly believed to depend on several moderating factors including the user's traits. Currently, there is a dearth of research on the…
Descriptors: Game Based Learning, Learner Engagement, Learning Motivation, Personality Traits
Hsu, Chia-Yu; Horikoshi, Izumi; Li, Huiyong; Majumdar, Rwitajit; Ogata, Hiroaki – Smart Learning Environments, 2023
The development of technology enables diverse learning experiences nowadays, which shows the importance of learners' self-regulated skills at the same time. Particularly, the ability to allocate time properly becomes an issue for learners since time is a resource owned by all of them. However, they tend to struggle to manage their time well due to…
Descriptors: Foreign Countries, Grade 7, Independent Study, Time Factors (Learning)
Idit Adler; Scott Warren; Cathleen Norris; Elliot Soloway – Smart Learning Environments, 2025
Smart learning environments provide students with opportunities to engage in self-regulated learning (SRL). However, little research has examined how teachers leverage these opportunities. We employed a multiple-case study methodology to examine the SRL supporting instructional practices of five third-grade teachers as they implemented a science…
Descriptors: Elementary School Students, Elementary School Teachers, Grade 3, Independent Study
Al Mamun, Md Abdullah; Lawrie, Gwendolyn – Smart Learning Environments, 2023
Technological innovations and changing learning environments are influencing student engagement more than ever before. These changing learning environments are affecting the constructs of student behavioural engagement in the online environment and require scrutiny to determine how to facilitate better student learning outcomes. Specifically,…
Descriptors: Instructional Design, Student Behavior, Inquiry, Learner Engagement
Beck Wells, Melissa – Smart Learning Environments, 2022
Universal design for learning (UDL) in higher education may be a useful tool in supporting the heterogenous higher education student population, specifically in supporting student academic outcomes and retention. With more students enrolling in digital formatted education, specifically international students, a strong framework must be established…
Descriptors: Higher Education, Access to Education, Academic Achievement, Undergraduate Students
Watcharapol Wiboolyasarin; Kanokpan Wiboolyasarin; Phornrat Tiranant; Poomipat Boonyakitanont; Nattawut Jinowat – Smart Learning Environments, 2024
Amidst the technological evolution shaping the landscape of education, this research critically examines the imperative factors influencing the design of language-teaching chatbots in Thai language classrooms. Employing a comprehensive two-pronged methodology, our study delves into the intricacies of chatbot design by engaging with a diverse…
Descriptors: Artificial Intelligence, Teaching Methods, Computer Software, Independent Study
Gambo, Yusufu; Shakir, Muhammad Zeeshan – Smart Learning Environments, 2021
Despite the increasing use of the self-regulated learning process in the smart learning environment, understanding the concepts from a theoretical perspective and empirical evidence are limited. This study used a systematic review to explore models, design tools, support approaches, and empirical research on the self-regulated learning process in…
Descriptors: Independent Study, Learning Processes, Electronic Learning, Learning Motivation
Chattaraj, Dishari; Vijayaraghavan, Arya Parakkate – Smart Learning Environments, 2021
The study, through the framework of mobility and space, explores the phenomenon of multiple shifts in learning spaces induced by COVID-19. The Interpretative Phenomenological Approach (IPA) is adopted to document the experiences and perceptions of learners caught within these spatial shifts--physical, online, and hybrid. Online interviews were…
Descriptors: COVID-19, Pandemics, Phenomenology, Educational Environment
Deepa, V.; Sujatha, R.; Mohan, Jitendra – Smart Learning Environments, 2022
Technology adoption for school education further gained momentum during the COVID-19 pandemic. However, the challenges and strategies of children belonging to the less privileged (we use 'privileged' in the article to identify those enjoying a standard of living or rights as majority of people in the society) families are different from those of…
Descriptors: Constructivism (Learning), Grounded Theory, Technology Integration, Living Standards

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