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Camille Dickson-Deane; Patricia Grant; Dauran McNeil – Distance Learning, 2024
Recognizing that online teaching and learning is placed within an environment where neither the teacher nor the student has complete-control is the first step to actioning capabilities towards a humanized environment. The tool that is typically used in higher education, the learning management system (LMS), has a design that constrains learning…
Descriptors: Learning Management Systems, Empathy, Personal Autonomy, Electronic Learning
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Bettinger, Eric; Fairlie, Robert; Kapuza, Anastasia; Kardanova, Elena; Loyalka, Prashant; Zakharov, Andrey – Journal of Policy Analysis and Management, 2023
The previous expansion of EdTech as a substitute for traditional learning around the world, the recent full-scale substitution due to COVID-19, and potential future shifts to blended approaches suggest that it is imperative to understand input substitutability between in-person and online learning. We explore input substitutability in education by…
Descriptors: Computer Assisted Instruction, Homework, Conventional Instruction, Achievement Gains
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Jasmin Breitwieser; Andreas B. Neubauer; Florian Schmiedek; Garvin Brod – npj Science of Learning, 2024
Mobile devices are ubiquitous, but their potential for adaptive educational interventions remains largely untapped. We identify three key promises of mobile interventions for educational research and practice: 1) intervening when it is most beneficial (i.e., "just-in-time adaptivity"), 2) estimating causal effects of interventions in…
Descriptors: Students, Handheld Devices, Computer Assisted Instruction, Educational Technology
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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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Shaikh, Rafikh Rashid; G., Nagarjuna; Gupta, Ayush – Education and Information Technologies, 2023
Networked computers can potentially support classrooms to be more interactive. It can help students share representations amongst themselves and work together on a shared virtual activity space. In research on the role of shared screens or shared virtual workspace in learning settings, there has been less attention paid to contexts where learners…
Descriptors: Technology Uses in Education, Computer Assisted Instruction, Computer Games, Mathematics Education
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Troussas, Christos; Giannakas, Filippos; Sgouropoulou, Cleo; Voyiatzis, Ioannis – Interactive Learning Environments, 2023
Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework…
Descriptors: Computer Assisted Instruction, Cognitive Style, Cooperative Learning, Models
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Bahari, Akbar; Wu, Sumei; Ayres, Paul – Educational Psychology Review, 2023
A contemporary review (over a 10-year period) was conducted into studies that used computer-assisted language learning (CALL) strategies to learn a second language (L2) by considering the impact of cognitive load. Twelve affordances were identified that led to enhanced learning, namely, online annotations and glosses, captioning, digital…
Descriptors: Computer Assisted Instruction, Second Language Learning, Cognitive Processes, Difficulty Level
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Zheng, Lanqin – Lecture Notes in Educational Technology, 2021
This book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design…
Descriptors: Data Use, Instructional Design, Computer Assisted Instruction, Cooperative Learning
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Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
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Sridharan, Shwetha; Saravanan, Deepti; Srinivasan, Akshaya Kesarimangalam; Murugan, Brindha – Education and Information Technologies, 2021
There exist numerous resources online to gain the desired level of knowledge on any topic. However, this complicates the process of selecting the most appropriate resources. Every learner differs in terms of their learning speed, proficiency, and preferred mode of learning. This paper develops an adaptive learning management system to tackle this…
Descriptors: Integrated Learning Systems, Computer Assisted Instruction, Individualized Instruction, Learning Analytics
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Torres-Jimenez, Jose; Lescano, Germán; Lara-Alvarez, Carlos; Mitre-Hernandez, Hugo – Education and Information Technologies, 2023
Conflicts play an important role to improve group learning effectiveness; they can be decreased, increased, or ignored. Given the sequence of messages of a collaborative group, we are interested in recognizing conflicts (detecting whether a conflict exists or not). This is not an easy task because of different types of natural language…
Descriptors: Conflict, Identification, Computer Assisted Instruction, Cooperative Learning
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James Lamb; Tim Fawns; Joe Noteboom; Jen Ross – Higher Education Research and Development, 2025
Ideas of space within higher education are changing, influenced by pedagogical innovation, emerging technologies, and the experiences of the COVID-19 pandemic. This is most obvious in the expansion of hybrid education, where teaching happens simultaneously both online and on the physical campus. Hybrid learning spaces emerge from dynamic,…
Descriptors: Foreign Countries, Graduate Students, College Faculty, Blended Learning
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Chen, Wenli; Tan, Jesmine S. H.; Zhang, Si; Pi, Zhongling; Lyu, Qianru – Educational Technology Research and Development, 2023
Nurturing twenty-first-century competency is one important agenda in this era, especially in developing collaborative learning and critical thinking skills. Yet, facilitating such a computer-supported collaborative learning (CSCL) environment is challenging. Although several technological platforms from past research studies were developed to…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Learning Analytics, Educational Technology
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Mubarak M. Aldawsari; Abdullah D. Alenezi; John I. Liontas – Reading Matrix: An International Online Journal, 2025
Artificial Intelligence (AI) has rapidly become a pivotal force in education, offering personalized learning pathways and dynamic solutions to longstanding instructional challenges. In English as a Foreign Language (EFL) contexts, idiomatic competence remains a challenging aspect of language development, often eluding effective coverage through…
Descriptors: Artificial Intelligence, Computer Software, Teaching Methods, Technology Integration
Eglington, Luke G.; Pavlik, Philip I., Jr. – Grantee Submission, 2022
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
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