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Yu Lei; Xin Fu; Jingjie Zhao; Baolin Yi – Education and Information Technologies, 2025
Grouping students according to their abilities and promoting deeper interaction and moderation are key issues in improving computational thinking in collaborative programming. However, the distribution characteristics and evolving pathways of computational thinking in different groups have not been deeply explored. During the course of a…
Descriptors: Ability Grouping, Computation, Programming, Cooperative Learning
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Sher Abbas; Liu Junqi; Liu Rongbing – Education and Information Technologies, 2025
This study examines the relationship between digital citizenship practices (DCP) and online student behavior (OSB). This study investigates the mediating role of digital technology engagement and technology use in the relationship between DCP and OSB. This study used a sample of 551 students from various schools and universities in China, and the…
Descriptors: Foreign Countries, Student Behavior, Technology Uses in Education, Educational Technology
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Caihong Feng; Jingyu Liu; Jianhua Wang; Yunhong Ding; Weidong Ji – Education and Information Technologies, 2025
Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…
Descriptors: Academic Achievement, Prediction, Models, Student Behavior
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Emine Cabi – Education and Information Technologies, 2025
Learning Management System (LMS) can track student interactions with digital learning resources during an online learning activity. Learners with different goals, motivations and preferences may exhibit different behaviours when accessing these materials. These different behaviours may further affect their learning performance. The purpose of this…
Descriptors: Academic Achievement, Electronic Learning, Learning Management Systems, Student Behavior
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Berkan Celik; Kursat Cagiltay – Education and Information Technologies, 2024
MOOC learners come from different backgrounds, and they have various motivations and intentions for taking online courses. This study examines learner intentions with subsequent behaviors and the reasons for the intention-behavior gap in MOOCs. A total of four MOOCs from BilgeIs MOOC Portal was used in this study. This quantitative study with a…
Descriptors: Students, MOOCs, Intention, Student Behavior
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Duraisamy Akila; Harish Garg; Souvik Pal; Sundaram Jeyalaksshmi – Education and Information Technologies, 2024
Online education has been expected to be the future of learning; it will never replace the value of traditional classroom experiences fully. Technical problems have less impact on offline education, which gives students more freedom to plan their time and stick to it. In addition, teachers cannot observe their students' behavior and activities…
Descriptors: In Person Learning, Student Behavior, Attention, Artificial Intelligence
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Zhipeng Zhou; Ziyao Zhang; Ying Lu; Zilong Wang; Jianqiang Cui; Guodong Ni – Education and Information Technologies, 2024
For working students, reconciling work and school lives is a major challenge. Emerging ubiquitous information and communication technologies (ICTs) further exacerbate this challenge, as a constant connection to work via ICTs blurring the boundaries between work and school domains. While the influence of ICTs on users' work and personal lives has…
Descriptors: Information Technology, Student Employment, Coping, Computer Use
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Francisco Pitthan; Kristof De Witte – Education and Information Technologies, 2025
Despite the potential for personalized learning, e-learning courses often suffer from low completion rates. In order to address this issue, we propose and empirically test a theoretical mechanism that examines how gamification can enhance the completion rate in adaptive learning courses by promoting a more positive behavioral response and attitude…
Descriptors: Gamification, Student Behavior, Student Attitudes, Financial Education
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Man Huang – Education and Information Technologies, 2025
As educational technology advances, the role of artificial intelligence (AI) in enhancing language education becomes increasingly prominent. However, there is a scarcity of empirical research assessing how AI integration influences student engagement and contributes to the language learning performance. This mixed-methods study seeks to fill the…
Descriptors: Foreign Countries, Middle School Students, Artificial Intelligence, Learner Engagement
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Li, Ling; Xiao, Jun – Education and Information Technologies, 2022
Existing research studying MOOC learner diversity has mainly taken unidimensional approaches, which have led to partial or inconsistent findings. This paper addresses this issue by proposing a multi-dimensional model that helps to identify and build the personas of key learner subgroups in any given MOOC course. By linking learners' behavioral…
Descriptors: Profiles, Online Courses, Student Characteristics, Student Behavior
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Anass Bayaga – Education and Information Technologies, 2025
This study examines the influence of AI-powered and emerging technologies on pedagogical practices in higher education, focusing on their role on behavioural intention (BI) and actual usage among educators and students. The research hypothesises that the relationship between each Unified Theory of Acceptance and Use of Technology (UTAUT)…
Descriptors: Artificial Intelligence, Educational Technology, Teaching Methods, Educational Innovation
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Fu Chen; Shan Li; Lijia Lin; Xiaoshan Huang – Education and Information Technologies, 2024
Social annotation plays a crucial role in nurturing and sustaining a collaborative reading community, offering the potential to enhance students' motivation and performance within socially supportive learning environments. Nonetheless, research on the dynamic changes in student engagement in social annotation remains limited. This study aims to…
Descriptors: Computer Mediated Communication, Documentation, Undergraduate Students, Reading Assignments
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Rochdi Boudjehem; Yacine Lafifi – Education and Information Technologies, 2024
Teaching Institutions could benefit from Early Warning Systems to identify at-risk students before learning difficulties affect the quality of their acquired knowledge. An Early Warning System can help preemptively identify learners at risk of dropping out by monitoring them and analyzing their traces to promptly react to them so they can continue…
Descriptors: At Risk Students, Identification, Dropouts, Student Behavior
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Aparisi, David; Delgado, Beatriz; Bo, Rosa María – Education and Information Technologies, 2023
Cyberbullying has generated interest for researchers in the field of psychology and education in recent years. While most studies have focused on samples of adolescents, the university environment also deserves special attention due to its serious consequences on students. It is therefore very important to prevent cyberbullying in the context of…
Descriptors: Bullying, Computer Mediated Communication, Victims, Anxiety
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Önder, Asuman; Akçapinar, Gökhan – Education and Information Technologies, 2023
The effective use of self-regulation strategies has been considered significant in online learning environments. It is known that learners must be supported in this context. Academic help-seeking (AHS), as one of the main self-regulated learning strategies, is associated with academic success. However, learners may avoid seeking help for…
Descriptors: Students, Help Seeking, Student Behavior, Learning Analytics
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