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Alexander Eitel; Marie-Christin Krebs; Claudia Schöne – Educational Psychology Review, 2025
Given the many opportunities for technology use in education nowadays (e.g., Large language models, explainer videos, digital quizzing), teachers should know and rely on evidence-based answers to questions about when, how, and why technology-augmented instruction helps or hinders learning. To date, finding these answers requires integrating…
Descriptors: Predictor Variables, Technology Uses in Education, Educational Technology, Computer Assisted Instruction
Anabela Anabela Malpique; Mustafa Asil; Deborah Pino-Pasternak; Susan Ledger; Timothy Teo – Reading and Writing: An Interdisciplinary Journal, 2025
Digital tools are an integral part of most writing communities across the globe, enhancing the criticality of gaining a comprehensive understanding of both paper and computer-based writing acquisition and development. The relationships between transcription skills and children's paper-based writing performance are well documented. Less is known…
Descriptors: Handwriting, Writing Skills, Keyboarding (Data Entry), Spelling
Casey J. Metoyer; Katherine Sullivan; Lee J. Winchester; Mark T. Richardson; Michael R. Esco; Michael V. Fedewa – Measurement in Physical Education and Exercise Science, 2025
Relative adiposity (%Fat) was measured using a smartphone-based application in a convenience sample of adults aged 20-52 years (n = 32, 68.7% female, 84.3% White/Caucasian, 26.7 ± 3.5 kg/m2) across different body positions (Anterior versus Posterior) on consecutive days (Day 1 versus Day 2). A reference photo was obtained from the posterior view…
Descriptors: Adults, Body Composition, Handheld Devices, Computer Assisted Instruction
Yavuz Akbulut; Onur Dönmez; Beril Ceylan; Tayfun Firat – Journal of Computing in Higher Education, 2025
Providing pre-training on new material can simplify complex content for learners who may need guidance to understand basic facts and organize their efforts. However, the effect of pre-training on learning outcomes is controversial because it tends to vary by context. Our aim was to investigate the effectiveness of pre-training in reducing…
Descriptors: Training, Cognitive Processes, Difficulty Level, Academic Achievement
Maryam Barkati; Zhila Kiyanfar; Mostafa Azari Noughabi; Fatemeh Ershadi – British Journal of Educational Technology, 2025
Despite the proliferation of studies on computer-assisted language learning, scant research attention has been paid to informal digital learning of English (IDLE) and its antecedents. Therefore, the current study aimed to investigate whether Iranian EFL learners' L2 grit, digital literacy and self-efficacy contributed to their IDLE. A total of 313…
Descriptors: Self Efficacy, Academic Persistence, English (Second Language), Digital Literacy
Bin Zou; Qinglang Lyu; Yining Han; Zijing Li; Weilei Zhang – Computer Assisted Language Learning, 2025
Adapted from the Technology Acceptance Model (TAM), the Integrated Model of Technology Acceptance (IMTA) has been used to examine the perceptions and acceptance of computer-assisted language learning (CALL), such as online learning, mobile learning, and learning management systems. However, whether IMTA can be applied to empirical research on…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Artificial Intelligence
Bahman Gorgian; Farzaneh Mir; Batool Nasiri – Iranian Journal of Language Teaching Research, 2025
The evaluation of in-service teacher educators ' feedback on using Computer-Assisted Language Learning (CALL) processes is important for providing educational policymakers with the strengths and weaknesses of the Vadana platform. It proposes an integrated CALL evaluation scale (ICES) eliciting the in-service teacher educators' literacy of using…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Teacher Educators

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