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Oscar Yecid Aparicio-Gómez; Olga Lucia Ostos-Ortiz; Constanza Abadía-García – Journal of Technology and Science Education, 2024
In today's educational environment, the convergence of emerging technologies and active methodologies has become a fundamental driver of change in university education. Emerging technologies, such as artificial intelligence, virtual reality, machine learning, and data analytics, are redefining the dynamics of higher education. Active…
Descriptors: Technological Advancement, Technology Uses in Education, Higher Education, Problem Based Learning
Krivova, Anna Leonidovna; Kalliopin, Alexander Konstantinovich; Korotaeva, Irina Eduardovna; Shafazhinskaya, Natalia Evgenievna; Ermilova, Daria Yuryevna – Journal of Educational Psychology - Propositos y Representaciones, 2021
In the era of the digital educational environment, where each participant of the educational process is actively involved in its development, the Internet and its services have become a popular tool. Open education network tools are defined as ICT tools that ensure the formation and maintenance of network electronic information resources of an…
Descriptors: Social Networks, Social Media, Educational Technology, Technology Integration
Orji, Fidelia A.; Vassileva, Julita; Greer, Jim – International Journal of Artificial Intelligence in Education, 2021
Persuasive Technologies (PT) are computational methods, strategies, and design techniques, grounded in social psychology to change user attitudes/behaviours. PTs have been applied in diverse areas, such as eCommerce, health, workplace, vehicles, urban and ambient environments. A kind of PT that has become popular in eLearning is known under the…
Descriptors: Intervention, Program Effectiveness, Learner Engagement, Class Size
Aziman Abdullah – International Society for Technology, Education, and Science, 2023
This study explores the potential of using screen time data in learning management systems (LMS) to estimate student learning time (SLT) and validate the credit value of courses. Gathering comprehensive data on actual student learning time is difficult, so this study uses LMS Moodle logs from a computer programming course with 490 students over 16…
Descriptors: Time Factors (Learning), Handheld Devices, Computer Use, Television Viewing
Lim, Lisa-Angelique; Dawson, Shane; Gaševic, Dragan; Joksimovic, Srecko; Pardo, Abelardo; Fudge, Anthea; Gentili, Sheridan – Assessment & Evaluation in Higher Education, 2021
Research and development in learning analytics has established viable solutions for scaling personalised feedback to all students. However, questions remain regarding how such feedback is perceived, interpreted and acted upon by stakeholders. The present study reports on the analysis of focus group data from four courses to understand students'…
Descriptors: Student Attitudes, College Students, Emotional Response, Individualized Instruction
Tsai, Yi-Shan; Perrotta, Carlo; Gaševic, Dragan – Assessment & Evaluation in Higher Education, 2020
The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related…
Descriptors: Student Empowerment, Equal Education, Learning Analytics, Accountability
Lluch Molins, Laia; Cano García, Elena – Journal of New Approaches in Educational Research, 2023
One of the main generic competencies in Higher Education is "Learning to Learn". The key component of this competence is the capacity for self-regulated learning (SRL). For this competence to be developed, peer feedback seems useful because it fosters evaluative judgement. Following the principles of peer feedback processes, an online…
Descriptors: Learning Analytics, Learning Management Systems, Peer Evaluation, Higher Education
Zhang, Jia-Hua; Zou, Liu-cong; Miao, Jia-jia; Zhang, Ye-Xing; Hwang, Gwo-Jen; Zhu, Yue – Interactive Learning Environments, 2020
Extensive studies have been conducted to diagnose and predict students' academic performance by analyzing a large amount of data related to their learning behaviors in a blended learning environment. But there is a lack of research examining how individualized learning interventions could improve students' academic performance in such a learning…
Descriptors: Individualized Instruction, Academic Achievement, Interaction, Blended Learning
Mao, Ye; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and…
Descriptors: Time, Models, Artificial Intelligence, Bayesian Statistics
Jitpaisarnwattana, Napat; Reinders, Hayo; Darasawang, Pornapit – Online Learning, 2021
In the last decade, there has been a great deal of interest in language MOOCs (LMOOCs) and their potential to offer learning opportunities for large audiences, including those in disadvantaged communities. However, experiences and research have shown MOOCs to suffer from several challenges. Chief among these have been low participation and…
Descriptors: Online Courses, Educational Technology, Technology Uses in Education, Second Language Learning
Whalley, Brian; France, Derek; Park, Julian; Mauchline, Alice; Welsh, Katharine – Higher Education Pedagogies, 2021
The concept of the Fourth Industrial Revolution is related to a ubiquitously connected, pervasively proximate (UCaPP) world and its response to COVID-19. Pedagogies need to be aligned with institutional 'quality education' and changes in the nature of the undergraduate student intake to formulate a 'Future Educational System'. Considerations…
Descriptors: Individualized Instruction, Active Learning, COVID-19, Pandemics
Agreda Montoro, Miriam; Ortiz Colón, Ana Mª; Rodríguez Moreno, Javier; Steffens, Karl – Digital Education Review, 2019
The convergence in the use of technology in classrooms and the development of new methodologies have involved a redefinition of the different educational agents' performance, for the upcoming Horizon reports to generate a radiography of the emerging technological trends that will have an impact in the upcoming years. As a consequence, we will…
Descriptors: Technological Advancement, Learning Analytics, Educational Technology, Cooperative Learning
Maaliw, Renato R., III – Online Submission, 2020
Most virtual learning environment fails to recognize that students have different needs when it comes to learning. With the evolving characteristics and tendencies of students, these learning environments must provide adaptation and personalization features for adaptive learning materials, course content and navigational designs to support…
Descriptors: Virtual Classrooms, Electronic Learning, Integrated Learning Systems, Individualized Instruction
Feng, Xuanqi; Yamada, Masanori – Educational Technology & Society, 2021
It is challenging to utilize learning analytic technologies to examine gameplay log data for game-embedded assessment in the field of game-based learning. Analytical approaches based on a new perspective focusing on complicated contextual data are imperative in the current scenario. A relatively new concept called precision education, which…
Descriptors: Learning Analytics, Behavior Patterns, Informal Education, Game Based Learning
Karaoglan Yilmaz, Fatma Gizem – Online Submission, 2020
The aim of this research is to examine the relationships between students' community of inquiry, academic self-efficacy, reflective thinking skills, problem-solving skills, and metacognitive awareness in a flipped learning environment supported by personalized recommendation and guidance messages based on learning analytics. For this purpose,…
Descriptors: Blended Learning, Learning Analytics, Feedback (Response), Individualized Instruction
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