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Xingsu Wu; Chunyang Xu – Education and Information Technologies, 2025
This study, anchored in the empirical domain of student learning experiences, employs the Chaoxing Fanya network teaching platform to delineate a comprehensive model of factors that influence student learning experiences within the framework of blended collaborative learning. Through a rigorous synthesis of extant literature and qualitative…
Descriptors: Learning Experience, Blended Learning, Cooperative Learning, College Students
Using Analytics to Predict Students' Interactions with Learning Management Systems in Online Courses
Ali Alshammari – Education and Information Technologies, 2024
In online education, it is widely recognized that interaction and engagement have an impact on students' academic performance. While previous research has extensively explored interactions between students, instructors, and content, there has been limited exploration of course design elements that promote the fourth type of interaction:…
Descriptors: Learning Analytics, Learning Management Systems, Academic Achievement, Correlation
Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
Gülay Öztüre Yavuz; Gökhan Akçapinar; Hatice Çirali Sarica; Yasemin Koçak Usluel – Education and Information Technologies, 2024
This study aims to develop a predictive model for predicting gifted students' engagement levels and to investigate the features that are important in such predictions. Features reflecting students' emotions, social-emotional learning skills, learning approaches and video-watching behaviours were used in the prediction models. The study group…
Descriptors: Secondary School Students, Academically Gifted, Gifted Education, Learner Engagement
Nayak, Padmalaya; Vaheed, Sk.; Gupta, Surbhi; Mohan, Neeraj – Education and Information Technologies, 2023
Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…
Descriptors: Predictor Variables, Academic Achievement, Data Collection, Information Retrieval
Inan, Fethi A.; Bolliger, Doris U. – Education and Information Technologies, 2023
The purpose of this study was to group instructors based on their patterns of implementing activities in their online courses, to examine factors that influenced differences within clusters, and to explore whether cluster membership affected instructor satisfaction. Data were collected from faculty at a university in the western United States with…
Descriptors: Web Based Instruction, Learning Activities, Online Courses, Teacher Attitudes
Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Granic, Andrina – Education and Information Technologies, 2022
During the past decades a respectable number and variety of theoretical perspectives and practical approaches have been advanced for studying determinants for prediction and explanation of user's behavior towards acceptance and adoption of educational technology. Aiming to identify the most prominent factors affecting and reliably predicting…
Descriptors: Educational Technology, Technology Integration, Predictor Variables, Electronic Learning
Chettaoui, Neila; Atia, Ayman; Bouhlel, Med Salim – Education and Information Technologies, 2023
Recent advances in sensor technology, including eye-gaze tracking, have introduced the opportunity to incorporate gaze into student modelling within an embodied learning context. The produced multimodal data is used to uncover cognitive, behavioural, and affective processes during the embodied learning activity. However, the use of eye-tracking…
Descriptors: Elementary School Students, Eye Movements, Academic Achievement, Human Body
Athitaya Nitchot; Lester Gilbert – Education and Information Technologies, 2025
Learning programming is a complex process that requires understanding abstract concepts and solving problems efficiently. To support and motivate students, educators can use technology-enhanced learning (TEL) in the form of visual tools for knowledge mapping. Mytelemap, a prototype tool, uses TEL to organize and visualize information, enhancing…
Descriptors: Learning Motivation, Concept Mapping, Programming, Computer Science Education
Anggraini, Merliyani Putri; Cahyono, Bambang Yudi; Anugerahwati, Mirjam; Ivone, Francisca Maria – Education and Information Technologies, 2022
The current research aimed to discover the most frequently used reading strategies of EFL university students across their reading proficiency and personality types and examine the interaction between the predictive factors in using the strategies when reading English online texts. Data were collected using a questionnaire on reading strategies…
Descriptors: Reading Comprehension, Personality Traits, English (Second Language), Second Language Learning
Ghai, Akanksha; Tandon, Urvashi – Education and Information Technologies, 2023
The current study investigates the interaction of Gamification, and Instructional Design to enhance the Usability of e-Learning in higher education programs. The study also examines the mediating role of Instructional design. Data were collected from a self-structured questionnaire from the academicians and was analyzed through Structural Equation…
Descriptors: Gamification, Instructional Design, Usability, Electronic Learning
Ayça Fidan; Yasemin Koçak Usluel – Education and Information Technologies, 2024
It is pointed out that one of the main problems of online learning environments is determining whether students engage or not. As engagement is a complex and multifaceted concept, researchers have stated that engagement is effected by many factors (environmental conditions and learner characteristics) and changes according to the context. Among…
Descriptors: Online Courses, Electronic Learning, Metacognition, Emotional Response
Balouchi, Shima; Samad, Arshad Abdul – Education and Information Technologies, 2021
There is a wealth of research investigating language learners' adoption of digital technology in higher education. To date, however, research primarily focuses on the implementation of technology in formal learning either inside or outside of the classroom, and the associations between factors influencing learners' informal English learning in…
Descriptors: Second Language Learning, English (Second Language), Informal Education, Intention
Humida, Thasnim; Al Mamun, Md Habib; Keikhosrokiani, Pantea – Education and Information Technologies, 2022
Digital transformation and emerging technologies open a horizon to a new method of teaching and learning and revolutionizes the e-learning industry. The goal of this study is to scrutinize a proposed research model for predicting factors that influence student's behavioral intention to use e-learning system at Begum Rokeya University, Bangladesh.…
Descriptors: Student Behavior, Intention, Electronic Learning, College Students