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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
Mengke Wang; Taotao Long; Na Li; Yawen Shi; Zengzhao Chen – Education and Information Technologies, 2025
Feedback plays an indispensable role in pre-service teachers' microteaching practice. It provides essential information about their microteaching performance, which is of great significance in their reflection and improvement. As AI and teaching analytics advance, feedback is no longer exclusively human-generated. AI technologies are increasingly…
Descriptors: Feedback (Response), Preservice Teachers, Microteaching, Reflection
Hatice Yildiz Durak – Education and Information Technologies, 2025
Feedback is critical in providing personalized information about educational processes and supporting their performance in online collaborative learning environments. However, giving effective feedback and monitoring its effects, which is especially important in online environments, is a complex issue. Although providing feedback by analyzing…
Descriptors: Feedback (Response), Online Systems, Electronic Learning, Learning Analytics
Khalid Alalawi; Rukshan Athauda; Raymond Chiong; Ian Renner – Education and Information Technologies, 2025
Learning analytics intervention (LAI) studies aim to identify at-risk students early during an academic term using predictive models and facilitate educators to provide effective interventions to improve educational outcomes. A major impediment to the uptake of LAI is the lack of access to LAI infrastructure by educators to pilot LAI, which…
Descriptors: Intervention, Learning Analytics, Guidelines, Prediction
Jelena Jovanovic; Andrew Zamecnik; Abhinava Barthakur; Shane Dawson – Education and Information Technologies, 2025
Higher education institutions are increasingly seeking ways to leverage the available educational data to make program and course quality improvements. The development of automated curriculum analytics can play a substantial role in this effort by bringing novel and timely insights into course and program quality. However, the adoption of…
Descriptors: Learning Analytics, Curriculum Evaluation, Evaluation Methods, Educational Objectives
Riina Kleimola; Laura Hirsto; Heli Ruokamo – Education and Information Technologies, 2025
Learning analytics provides a novel means to support the development and growth of students into self-regulated learners, but little is known about student perspectives on its utilization. To address this gap, the present study proposed the following research question: what are the perceptions of higher education students on the utilization of a…
Descriptors: Self Management, College Students, Learning Analytics, Student Development
Anitia Lubbe; Elma Marais; Donnavan Kruger – Education and Information Technologies, 2025
Amalgamating generative artificial intelligence (Gen AI), Bloom's taxonomy and critical thinking present a promising avenue to revolutionize assessment pedagogy and foster higher-order cognitive skills needed for learning autonomy in the domain of self-directed learning. Gen AI, a subset of artificial intelligence (AI), has emerged as a…
Descriptors: Critical Thinking, Computer Software, Learning Analytics, Intelligent Tutoring Systems
Christopher C. Y. Yang; Jiun-Yu Wu; Hiroaki Ogata – Education and Information Technologies, 2025
Blended learning (BL) combines traditional classroom activities with online learning resources, enabling students to obtain higher academic performance through well-defined interactive learning strategies. However, lacking the capacity to self-regulate their learning, many students might fail to comprehensively study the learning materials after…
Descriptors: Blended Learning, Educational Technology, Learning Analytics, Self Management
Xintong Zhang; Jiangwei Hu; Yunqian Zhou – Education and Information Technologies, 2025
This study explores the role of perceived utility, social influence, and ethical concerns in the adoption of AI-based data analysis tools among academic researchers in China, focusing on differences between public and private universities. The research aims to identify key drivers and barriers influencing the integration of AI technology in…
Descriptors: Usability, Ethics, Artificial Intelligence, Technology Uses in Education
Melis Dülger; Anouschka van Leeuwen; Jeroen Janssen; Liesbeth Kester – Education and Information Technologies, 2025
Self-regulated learning (SRL) is crucial for fostering lifelong learning skills in students, encompassing planning, monitoring, and controlling abilities. Previous research indicates that many students struggle to regulate their learning effectively. In the Netherlands, adaptive learning technologies are widely used to support math education in…
Descriptors: Educational Technology, Technology Uses in Education, Elementary School Teachers, Direct Instruction
Yuchun Zhong; Jie Lian; Hao Huang; Hao Deng – Education and Information Technologies, 2025
This study investigated the affordances, constraints, and implications of ChatGPT in education using the affordance theory and social-ecological systems theory. We employed a data mining approach that blends social media analytics including sentiment analysis and topic modelling and qualitative analysis to extract viewpoints from a collection of…
Descriptors: Affordances, Barriers, Technology Uses in Education, Artificial Intelligence
Zhicheng Dai; Yue Yang; Zengzhao Chen; Ling Wang; Liang Zhao; Xiaoliang Zhu; Junxia Xiong – Education and Information Technologies, 2025
Higher education is beginning to focus on how to effectively cultivate IoT engineers who possess both hard skills and soft skills. Therefore, from the perspective of activity theory and combining it with project-based learning, this study constructed a project-based learning framework based on activity theory and applied this framework to an IoT…
Descriptors: Active Learning, Student Projects, Teacher Effectiveness, Instructional Effectiveness
Yunus Kökver; Hüseyin Miraç Pektas; Harun Çelik – Education and Information Technologies, 2025
This study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design…
Descriptors: Artificial Intelligence, Misconceptions, Preservice Teachers, Natural Language Processing
A Learning Assistance Framework for Supporting and Analyzing Student Teams in Project-Based Learning
Youcef Haddou; Omar Talbi; Abdelkader Ouared; Abdelhafid Chadli – Education and Information Technologies, 2025
As Project-Based Learning (PjBL) becomes a cornerstone of modern education, effective support and analysis tools are crucial for enhancing collaborative learning experiences and achieving educational goals. However, challenges such as managing overload related to the rigor and demands of project-based activities (e.g., meetings, writing reports,…
Descriptors: Student Projects, Active Learning, Guidelines, Teamwork
Yuxiao Xie; Ziyi Xie; Siyu Chen; Lei Shen; Zhizhuang Duan – Education and Information Technologies, 2025
The National College English Test Band 4 (CET-4) is a key test to assess the English language ability of Chinese university students, and the success rate of the test is important to improve the quality of their English learning. Artificial intelligence technology can be used to predict and explore the factors influencing the success rate. This…
Descriptors: Language Tests, English (Second Language), Second Language Learning, Second Language Instruction
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