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Jacob Davidsen; Rolf Steier – International Journal of Research & Method in Education, 2025
Methodological advancements for the study of learning processes are both shaped by and drivers of technology developments. Interaction Analysis (IA), as a core methodological approach over the past three decades is reflective of this relationship and can be understood by examining moves from analogue video tapes, to digital media, computational…
Descriptors: Educational Research, Interaction, Research Methodology, Computer Use
Betul Aydin; Suleyman Sadi Seferoglu – Turkish Online Journal of Distance Education, 2025
This study aims to conduct validity and reliability of a measurement tool developed to determine university students' levels of digital risk taking. 646 undergraduate students from 8 different universities voluntarily participated in the study. Exploratory and confirmatory factor analyses were conducted to reveal the factor structure of the…
Descriptors: Undergraduate Students, Measures (Individuals), Risk, Test Validity
Khalida Parveen; Abdulelah A. Alghamdi; Nagwan Abdel Samee; Muhammad Shafiq – Journal of Educational Computing Research, 2025
As technology rapidly evolves, generative AI tools are increasingly integrated across various fields, including education. ChatGPT, a well-known language model developed by OpenAI, has gained significant importance in educational settings. This study employed a quantitative, cross-sectional survey design and employed the Unified Theory of…
Descriptors: Artificial Intelligence, Computer Uses in Education, College Students, Foreign Countries
Sharon R. Mittiga; Nerelie C. Freeman; Brett E. Furlonger; Perrin Chan; Erin S. Leif – Journal of Positive Behavior Interventions, 2025
This study evaluated the quality and behavior change techniques (BCTs) included in 11 freely available mobile classroom behavior management applications (mCBM apps). We found that mCBM apps included a limited number of BCTs, with an average of 9 of 21 possible BCTs. Consequence-based BCTs like rewards and feedback were common, while…
Descriptors: Behavior Modification, Student Behavior, Computer Uses in Education, Positive Behavior Supports
Zien Ding; Ru-De Liu; Yi Ding; Xiantong Yang; Yi Yang – Education and Information Technologies, 2025
Academic cyberloafing, defined as the involvement in non-academic online activities during academic tasks, has emerged as a prevalent concern within higher education. While previous research has identified course-related factors that may influence academic cyberloafing, the specific relation between perceived course difficulty and academic…
Descriptors: Course Selection (Students), Difficulty Level, Computer Use, Time Management
Yu Gou; Luyan Teng; Haohong Nie – SAGE Open, 2025
The rapid development of the Internet has made search engines the preferred way for people to obtain information. As the world's largest Chinese search engine, Baidu Index developed by Baidu is an important data analysis platform. Similar to Google Trends, Baidu Index is a data tool based on data that provides the display frequency and related…
Descriptors: Bullying, Search Engines, Trend Analysis, Geographic Location
Aleksandra Kobicheva; Elena Tokareva; Tatiana Baranova – European Journal of Psychology of Education, 2025
Phubbing is not only a consequence of technological advancements but also represents an entirely novel aspect of social conduct, impacting students' academic performance and the sustainability of development. The purpose of the study is to identify the relationship between students' level of phubbing, academic engagement and academic performance…
Descriptors: Student Behavior, Learner Engagement, Computer Use, College Students
Anett Wolgast; Stefan Bieletzke – European Journal of Psychology and Educational Research, 2025
Research on academic outcomes has extensively explored students' emotions, motivation, and learning behavior. While further research highlights the role of individual interactions with generative artificial intelligence (AI), a gap exists in understanding the longitudinal dynamic associations between university students' interactions with AI…
Descriptors: Artificial Intelligence, College Students, Student Attitudes, Computer Uses in Education
Aaron T. Berger; Darin J. Erickson; Kayla T. Johnson; Emma Billmyer; Kyla Wahlstrom; Melissa N. Laska; Rachel Widome – Journal of School Health, 2025
Background: We aimed to characterize relationships between delayed high school start time policy, which is known to lengthen school night sleep duration, and patterns in activity outcomes: physical activity, non-school electronic screen time (non-schoolwork), and sports and extracurricular activity among adolescents. Methods: We used data from the…
Descriptors: School Schedules, High School Students, Physical Activity Level, Computer Use
Nicole K. Iappelli – Online Submission, 2025
The purpose of this study was to examine how screen time and peer interaction affect student focus and social engagement across K-12 classrooms, particularly in light of changes observed since the COVID-19 pandemic. This study collected survey data from K-12 educators across three grade bands (K-4, 5-8, and 9-12) to assess students' device use…
Descriptors: Elementary Secondary Education, Computer Use, Peer Relationship, Interaction
Suliman Zakaria Suliman Abdalla; Amal Khalfan Rashid AlSalti – Educational Process: International Journal, 2025
Background/purpose: This study examines the behavioral factors influencing the adoption of AI-powered generative technologies in higher education and their impact on students' cognitive engagement--a crucial element of sustainable, inclusive, and high-quality learning, as envisioned by UNESCO's Sustainable Development Goal 4 (SDG4). The study…
Descriptors: Artificial Intelligence, Cognitive Development, College Students, Computer Uses in Education
Jamie M. Chen; Limin Zhang; Supavich Pengnate; Emily Ma; Xi Yu Leung – Journal of Information Systems Education, 2025
Although e-learning is considered one of the leading teaching methods in higher education, both learners and instructors face significant challenges owing to reduced social interaction compared with traditional classroom learning. In this study, we explore the leveraging of recent developments in generative artificial intelligence (AI) and create…
Descriptors: Artificial Intelligence, Computer Uses in Education, Electronic Learning, Learner Engagement
Isabelle Cabot; Rachel Surprenant – Higher Education Studies, 2025
Numerous studies have reported a disruption in the lifestyle habits (LsHs) of post-secondary students during the COVID-19 pandemic. Given the importance of LsHs for these young adults' physical and mental health and academic success, it is pertinent to examine whether these changes were maintained beyond the public health crisis. As post-pandemic…
Descriptors: Foreign Countries, College Students, Life Style, Student Behavior
Pranjli Khanna; Kaleb Mathieu; Kole Norberg; Husni Almoubayyed; Stephen E. Fancsali – International Educational Data Mining Society, 2025
Recent research on more comprehensive models of student learning in adaptive math learning software used an indicator of student reading ability to predict students' tendencies to engage in behaviors associated with so-called "gaming the system." Using data from Carnegie Learning's MATHia adaptive learning software, we replicate the…
Descriptors: Computer Software, Computer Uses in Education, Reading Difficulties, Reading Skills
Hermann Astleitner; Sarah Schlick – Active Learning in Higher Education, 2025
Social media has a strong influence on the everyday lives of college students. A particular question of advanced research interest is whether social media also play a role when attending class. This exploratory study was aimed at designing a theoretical model that embraces such concepts. First, we identified that identity development, learning…
Descriptors: Social Media, College Students, Self Concept, Computer Use
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