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Zhennan Sun; Mingyong Pang; Yi Zhang – Education and Information Technologies, 2025
The evolution of individual and global learning preferences is influenced by correlation factors. This study introduces a novel evolutionary modeling approach to observe and analyze factors that affect the evolution of learning preferences. The influencing factors considered in this study are closely interwoven with the underlying personality of…
Descriptors: Learning Analytics, Learning Processes, Preferences, Student Characteristics
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Xinyu Li; Yizhou Fan; Tongguang Li; Mladen Rakovic; Shaveen Singh; Joep van der Graaf; Lyn Lim; Johanna Moore; Inge Molenaar; Maria Bannert; Dragan Gaševic – Journal of Learning Analytics, 2025
The focus of education is increasingly on learners' ability to regulate their own learning within technology-enhanced learning environments. Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to productively self-regulate their learning, as they typically need to…
Descriptors: Learning Analytics, Metacognition, Independent Study, Skill Development
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Ramy Elmoazen; Mohammed Saqr; Laura Hirsto; Matti Tedre – International Journal of Computer-Supported Collaborative Learning, 2025
In collaborative learning, students may follow different trajectories that evolve over time. This study used a multilayered approach to map the temporal dynamics of online problem-based learning (PBL) and the transition of students' roles across time over a full year duration. Based on data from 135 dental students across four consecutive courses…
Descriptors: Cooperative Learning, Communities of Practice, Inquiry, Time Factors (Learning)
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De-Chih Lee; Ching-Yi Chang – Education and Information Technologies, 2025
During the digital age, self-directed learning has emerged as an effective teaching strategy in designing class activities within the educational sphere, prompting an investigation into its impact on the effectiveness of instruction in the K-12 context. This article presents a meta-analysis aimed at examining the efficacy of self-directed learning…
Descriptors: Elementary School Students, Secondary School Students, Independent Study, Active Learning
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Guoqian Luo; Hengnian Gu; Xiaoxiao Dong; Dongdai Zhou – Education and Information Technologies, 2025
In the realm of e-learning, supporting personalized learning effectively necessitates recommending sequences of learning items that maximize learning efficiency while minimizing cognitive load, all tailored to the learner's goals. These recommendations must account for the prerequisite relationships among learning items and the learner's…
Descriptors: Electronic Learning, Individualized Instruction, Sequential Learning, Learning Processes
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Fengjiao Tu; Linjing Wu; Kinshuk; Junhua Ding; Haihua Chen – Education and Information Technologies, 2025
With the development of information and communication technology, project-based learning (PBL) has become an important pedagogical approach. Group leaders are critical in PBL, and prestige influences learner leadership. Regulation affects learners' prestige, but research on their relationship is lacking. Through content analysis and epistemic…
Descriptors: Learning Processes, Reputation, Active Learning, Student Projects
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Ahmed A. Alsayer; Jonathan Templin; Chris Niileksela; Bruce B. Frey – Education and Information Technologies, 2025
Prior research on the "Community of Inquiry" (CoI) framework has a limited amount of work which uses structural techniques to confirm the factorial structure of the CoI. The current study investigates the structural relationships among the three elements of the CoI framework (cognitive presence, teaching presence, and social presence),…
Descriptors: Communities of Practice, Inquiry, Online Courses, Educational Experience
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Jinhee Kim; Rita Detrick; Seongryeong Yu; Yukyeong Song; Linda Bol; Na Li – Education and Information Technologies, 2025
Supporting learners in achieving high-level socially shared regulation of learning (SSRL) in the online collaborative learning (OCL) context presents challenges that the utilization of artificial intelligence (AI) technologies may help solve. However, the effective uses of AI to support multifaceted areas (cognition, metacognition, and motivation)…
Descriptors: Artificial Intelligence, Technology Uses in Education, Electronic Learning, Cooperative Learning
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Yiqiu Zhou; Jina Kang; Yeyu Wang; Muhammad Ashiq – Educational Technology Research and Development, 2025
The complex processes of collaborative knowledge construction require a multimodal approach to capture the interplay between learners, tools, and the environment. While existing studies have recognized the importance of considering multiple modalities, there remains a need for a comprehensive framework that explicitly models the dynamics of…
Descriptors: Cooperative Learning, Computer Simulation, Astronomy, Network Analysis
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Aleksandra Stalmach; Paola D'Elia; Sergio Di Sano; Gino Casale – Open Education Studies, 2024
This study investigates 14 digitally enhanced learning methods, shedding light on students with special educational needs (SEN) in inclusive digital learning environments. We seek to fill the gap in the literature by specifically investigating methods suitable for students with SEN. A survey among experts has been carried out to assess learning…
Descriptors: Inclusion, Learning Processes, Special Needs Students, Educational Technology
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Gwo-Jen Hwang; Chun-Chun Chang; Chin-Ya Juan – Educational Technology Research and Development, 2025
Problem-posing has been regarded as a potential method to engage students in deeper thinking; however, without sufficient supports, most students could encounter difficulties in posing quality problems. In this study, a concept mapping-based online cooperative problem-posing (CM-OCPP) approach is proposed to guide students to complete…
Descriptors: Learning Processes, Learning Strategies, Concept Mapping, Electronic Learning
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Jessica M. Karch; Nicolette M. Maggiore; Jennifer R. Pierre-Louis; Destiny Strange; Vesal Dini; Ira Caspari-Gnann – Science Education, 2024
Small group interactions and interactions with near-peer instructors such as learning assistants serve as fertile opportunities for student learning in undergraduate active learning classrooms. To understand what students take away from these interactions, we need to understand how and what they learn during the moment of their interaction. This…
Descriptors: Teaching Assistants, Interaction, Active Learning, Electronic Learning
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Yingbin Zhang; Luc Paquette; Nigel Bosch – International Journal of Artificial Intelligence in Education, 2025
Understanding the transitions among affective states during computer-based learning may guide the design of affect-responsive learning environments. Current studies have focused on the marginal strength of an affect transition, which is the average transition tendency over possible affective states preceding the transition. However, marginal…
Descriptors: Affective Behavior, Emotional Response, Electronic Learning, Learning Experience
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Albert Rof; Andrea Bikfalvi; Pilar Marques – TechTrends: Linking Research and Practice to Improve Learning, 2025
Microlearning is gaining ground in the higher education domain. Despite this trend, there is a lack of evidence of effectiveness when a large number of microlearning units are grouped to form a macrolearning programme. The purpose of this paper is to explore how and why a macrolearning affects students' self-efficacy. The originality of this paper…
Descriptors: Business Education, Teaching Methods, Computer Assisted Instruction, Critical Thinking
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Dahwi Ahn; Jason C. K. Chan – Applied Cognitive Psychology, 2025
COVID-19 greatly increased the online delivery of higher education. But one limitation of online learning is that students often struggle to stay engaged while watching online lectures. We examined whether including an instructor's face in lecture videos (instructor visibility) enhances student engagement or learning. In two preregistered…
Descriptors: Learner Engagement, Video Technology, Lecture Method, Student Attitudes
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