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Niklas Humble; Jonas Boustedt; Hanna Holmgren; Goran Milutinovic; Stefan Seipel; Ann-Sofie Östberg – Electronic Journal of e-Learning, 2024
Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant…
Descriptors: Cheating, Artificial Intelligence, Technology Uses in Education, Computer Science Education
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Khanittha Sittitiamjan; Pongpisit Wuttidittachotti – Educational Process: International Journal, 2025
Background/purpose: This study investigates how knowledge, attitudes, and practices (KAP) influence cybersecurity awareness (CSA) among students in Thai educational institutions. The research addresses regional disparities in cybersecurity readiness by incorporating a culturally responsive adaptation of the KAP model. Materials/methods: A…
Descriptors: Computer Security, Computer Science Education, Foreign Countries, College Students
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Shah, Zohal; Chen, Chen; Sonnert, Gerhard; Sadler, Philip M. – AERA Online Paper Repository, 2023
Computer gameplay and social media are the two most common forms of entertainment in the digital age. Many scholars share the assumption that leisure-time digital consumption is associated with CS affinity, but there is a dearth of research evidence for this relationship. Female students generally spend less time on gaming and more time on social…
Descriptors: Computer Science, Vocational Interests, Computer Use, Gender Differences
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Qian Fu; Wenjing Tang; Yafeng Zheng; Haotian Ma; Tianlong Zhong – Interactive Learning Environments, 2024
In this study, a predictive model is constructed to analyze learners' performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners' programming strategies.…
Descriptors: Predictor Variables, Psychological Patterns, Programming, Self Management
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Yesilyurt, Etem; Vezne, Rabia – Education and Information Technologies, 2023
Even though there is an abundance of research on computer supported education (CSE), digital literacy (DL), technological literacy (TL), and internet literacy (IL), the correlation between them and their effect on each other have not been analyzed in the literature. However, no study has been conducted on the correlation between and effect of CSE,…
Descriptors: Preservice Teachers, Digital Literacy, Technological Literacy, Internet
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Xiaoming Cao; Zhuo Huang; Junchen Wu; Mingzhu Li; Tao He – Education and Information Technologies, 2025
Cyberbullying has garnered growing attention, yet existing research lacks nuanced insights into the dynamics of students' cyberbullying profiles and the associated risk factors across multiple domains. This study aims to (1) investigate K-12 students' cyberbullying profiles, (2) develop an AI predictive model for cyberbullying roles, and (3)…
Descriptors: Bullying, Computer Mediated Communication, Victims, Artificial Intelligence
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Rasha Noureddine; David Boote; Laurie O. Campbell – Education and Information Technologies, 2025
A meta-analytic study was conducted of the "Unified Theory of Acceptance and Use of Technology" to understand higher education instructors use of technology in teaching and learning. Through broad searches of the years 2003-2023, 117 records were identified for initial review. After reviewing the studies and removing those studies that…
Descriptors: College Faculty, Teacher Attitudes, Technological Literacy, Computer Literacy
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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
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Bowen Xiao; Wanfen Chen; Xiaolong Xie; Hong Zheng; Danielle Law; Hezron Onditi; Junsheng Liu; Jennifer Shapka – International Journal of Bullying Prevention, 2025
The goal of the present study was to identify predictive factors related to cyberbullying by using supervised machine learning in a sample of Chinese adolescents during the COVID-19 pandemic. Participants included 2053 (M[subscript age]=16.36 years, SD = 1.14 years; 44.6% boys) adolescents from Fujian province, China. Data on cyberbullying,…
Descriptors: Bullying, Computer Mediated Communication, Predictor Variables, Artificial Intelligence
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Sacide G. Mazman Akar; Murat Özer – Journal of Pedagogical Research, 2025
This study explores the influence of digital literacy, parental styles, and background variables on digital game addiction among elementary school students. Given the formative nature of early childhood and the significant role of parental influence, understanding these relationships is essential to promoting healthy digital habits. The sample…
Descriptors: Video Games, Computer Games, Addictive Behavior, Elementary School Students
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Ahmed Alsayer – International Journal of Web-Based Learning and Teaching Technologies, 2025
The current research sought to identify important student achievement predictors from past literature, then use a publicly available dataset to assess the statistical contribution of technology factors above and beyond previously established effects. Using TIMSS data from Saudi Arabia, with data from 5,453 students in fourth grade mathematics and…
Descriptors: Grade 4, Elementary School Students, Foreign Countries, Mathematics Achievement
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Yavuz Akbulut; Onur Dönmez; Beril Ceylan; Tayfun Firat – Journal of Computing in Higher Education, 2025
Providing pre-training on new material can simplify complex content for learners who may need guidance to understand basic facts and organize their efforts. However, the effect of pre-training on learning outcomes is controversial because it tends to vary by context. Our aim was to investigate the effectiveness of pre-training in reducing…
Descriptors: Training, Cognitive Processes, Difficulty Level, Academic Achievement
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Umar Bin Qushem; Solomon Sunday Oyelere; Gökhan Akçapinar; Rogers Kaliisa; Mikko-Jussi Laakso – Technology, Knowledge and Learning, 2024
Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine…
Descriptors: Predictor Variables, Learning Analytics, At Risk Students, Computer Science
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Abdulrahman M. Al-Zahrani – International Journal of Virtual and Personal Learning Environments, 2023
This study explores the potential of social learning platforms (SLPs) to enhance female students' learning experiences in Saudi higher education and address socio-cultural constraints. It examines the advantages and disadvantages of SLPs from the perspective female students and analyses the impact of personal beliefs on their perceptions of SLPs.…
Descriptors: Females, Higher Education, Socialization, Empowerment
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Ting Zhang; Paul Bailey; Yuqi Liao; Emmanuel Sikali – Large-scale Assessments in Education, 2024
The EdSurvey package helps users download, explore variables in, extract data from, and run analyses on large-scale assessment data. The analysis functions in EdSurvey account for the use of plausible values for test scores, survey sampling weights, and their associated variance estimator. We describe the capabilities of the package in the context…
Descriptors: National Competency Tests, Information Retrieval, Data Collection, Test Validity
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