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Zainil, Melva; Kenedi, Ary Kiswanto; Rahmatina; Indrawati, Tin; Handrianto, Ciptro – Journal of Education and e-Learning Research, 2023
The aim of the current research is to develop a STEM-based digital classroom learning model based on the 21st-century skills and characteristics of elementary school students. To enable the dissemination of this learning model, further research is needed to determine the effect of the STEM-based digital classroom learning model on the 21st century…
Descriptors: Foreign Countries, Elementary School Students, STEM Education, Learning
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Ulrike Cress; Joachim Kimmerle – International Journal of Computer-Supported Collaborative Learning, 2023
Generative Artificial Intelligence (AI) tools, such as ChatGPT, have received great attention from researchers, the media, and the public. They are gladly and frequently used for text production by many people. These tools have undeniable strengths but also weaknesses that must be addressed. In this squib we ask to what extent these tools can be…
Descriptors: Artificial Intelligence, Cognitive Style, Computer Assisted Instruction, Learning Strategies
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Silvia-Jessica Mostacedo-Marasovic; Amanda A. Olsen; Cory T. Forbes – Journal of Science Education and Technology, 2024
Global climate change (GCC) is one of the greatest challenges of our age and a highly significant socio-scientific issue (SSI). Developing secondary students' understanding about the Earth's climate and GCC is critical for empowering future citizens and a key focus of the "Next Generation Science Standards" (NGSS Lead States, 2013). In…
Descriptors: Climate, Change, Secondary School Students, Evidence
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Shin, Hye Won; Sok, Sarah – ReCALL, 2023
The current study is an approximate replication of Gray and DiLoreto's (2016) study, which proposed a model predicting that course structure, learner interaction and instructor presence would influence students' perceived learning and satisfaction in online learning, with student engagement acting as a mediator between two of the predictors and…
Descriptors: College Students, Second Language Learning, Computer Assisted Instruction, Electronic Learning
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HaeJin Lee; Nigel Bosch – International Journal of STEM Education, 2024
Self-regulated learning (SRL) strategies can be domain specific. However, it remains unclear whether this specificity extends to different subtopics within a single subject domain. In this study, we collected data from 210 college students engaged in a computer-based learning environment to examine the heterogeneous manifestations of learning…
Descriptors: Computer Assisted Instruction, Self Management, Intellectual Disciplines, College Students
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Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam – IEEE Transactions on Learning Technologies, 2017
Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…
Descriptors: Computer Assisted Instruction, Problem Solving, Learning, Student Behavior
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Baker, Joseph M.; Martin, Taylor; Aghababyan, Ani; Armaghanyan, Armen; Gillam, Ronald – Technology, Knowledge and Learning, 2015
Advances in educational neuroscience have made it possible for researchers to conduct studies that observe concurrent behavioral (i.e., task performance) and neural (i.e., brain activation) responses to naturalistic educational activities. Such studies are important because they help educators, clinicians, and researchers to better understand the…
Descriptors: Computer Games, Mathematics, Neurosciences, Brain
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Chen, C.-H.; Chou, M.-H. – Journal of Computer Assisted Learning, 2015
Facing students' decreasing motivation to pursue scientific study, schools and educators need to coordinate new technologies with pedagogical agents to effectively sustain or promote students' scientific learning and motivation to learn. Although the provision of pedagogical agents in student learning has been studied previously, it is not clear…
Descriptors: Middle School Students, Learning, Student Motivation, Science Instruction
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Bellard, Breshanica – Journal of Educational Multimedia and Hypermedia, 2018
Professionals responsible for the delivery of education and training using technology systems and platforms can facilitate complex learning through application of relevant strategies, principles and theories that support how learners learn and that support how curriculum should be designed in a technology based learning environment. Technological…
Descriptors: Computer Assisted Instruction, Learning, Blended Learning, Pedagogical Content Knowledge
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van Borkulo, Sylvia P.; van Joolingen, Wouter R.; Savelsbergh, Elwin R.; de Jong, Ton – Journal of Science Education and Technology, 2012
Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode…
Descriptors: Direct Instruction, Computer Assisted Instruction, Climate, Task Analysis
Faleye, Sunday – Online Submission, 2011
This research report presents a new teaching and learning model in engineering classes. The proposed learning model is called the CCAILM (constructionist computer aided instructional learning model). This new model was derived from the constructionist learning theory, the media-affects-learning hypothesis and the multiple representation principle.…
Descriptors: Engineering Education, Undergraduate Study, Computer Assisted Instruction, College Instruction
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Jarvela, Sanna; Hadwin, Allyson F. – Educational Psychologist, 2013
Despite intensive research in computer-supported collaborative learning (CSCL) over the last decade, there is relatively little research about how groups and individuals in groups engage, sustain, support, and productively regulate collaborative processes. This article examines the role of regulatory processes in collaborative learning and how…
Descriptors: Cooperative Learning, Computer Assisted Instruction, Learning, Computer Mediated Communication
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Bowen, William G.; Chingos, Matthew M.; Lack, Kelly A.; Nygren, Thomas I. – Journal of Policy Analysis and Management, 2014
Online instruction is quickly gaining in importance in U.S. higher education, but little rigorous evidence exists as to its effect on student learning. We measure the effect on learning outcomes of a prototypical interactive learning online statistics course by randomly assigning students on six public university campuses to take the course in a…
Descriptors: Online Courses, Interaction, Outcomes of Education, Public Colleges
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Kast, Monika; Baschera, Gian-Marco; Gross, Markus; Jancke, Lutz; Meyer, Martin – Annals of Dyslexia, 2011
Our spelling training software recodes words into multisensory representations comprising visual and auditory codes. These codes represent information about letters and syllables of a word. An enhanced version, developed for this study, contains an additional phonological code and an improved word selection controller relying on a phoneme-based…
Descriptors: Spelling Instruction, Computer Assisted Instruction, Dyslexia, Computer Software
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Thoms, Charlotte L. V.; Burton, Sharon L. – International Journal of Adult Vocational Education and Technology, 2016
Continuous learning is essential in academic and business environments for the 21st century learner as success, survival, and growth lean toward the educator answering the question, "Which educational design best facilitates educators in becoming learner-focused while producing adaptive completers in this ubiquitous learning…
Descriptors: Educational Development, Success, Learning, Training Methods
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