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Daisy Das; Masum Ahmed – E-Learning and Digital Media, 2024
Many educational institutions lack well-defined, targeted policies to address problems relating to student smartphone use on campus. In this study, we analyse the patterns of student smartphone use on academic campuses and propose a range of policy measures to address the problems arising from such use. Our research, which draws on primary data…
Descriptors: Student Attitudes, Telecommunications, Handheld Devices, Technology Uses in Education
Maarten Sluijs; Uwe Matzat – Journal of Computer Assisted Learning, 2024
Background: Technological innovations such as Learning Management Systems (LMS) are becoming more and more prevalent in the learning environments of students. Distilling and acting on knowledge gathered from these systems, the field known as learning analytics, allows educators to hone their craft and support students more effectively by providing…
Descriptors: Time Management, Learning Analytics, Learning Management Systems, Predictive Measurement
Hector Vargas; Ruben Heradio; Gonzalo Farias; Zhongcheng Lei; Luis de la Torre – IEEE Transactions on Education, 2024
Contribution: A competency assessment framework that enables learning analytics for course monitoring and continuous improvement. Our work fills the gap in systematic methods for competency assessment in higher education. Background: Many institutions are shifting toward competency-based education (CBE), thus encouraging their educators to start…
Descriptors: Competency Based Education, Learning Analytics, Higher Education, College Students
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
Grace Leah Akinyi; Robert Oboko; Lawrence Muchemi – Electronic Journal of e-Learning, 2024
The future of university learning in Sub-Saharan Africa has become increasingly digitally transformed by both e-Learning, and learning analytics, post-COVID-19 pandemic. Learning analytics intervention is critical for effective support of socially-shared regulated learning skills, which are crucial for twenty-first-century e-Learners.…
Descriptors: Electronic Learning, Student Attitudes, Learning Analytics, Feedback (Response)
Saleem Malik; K. Jothimani – Education and Information Technologies, 2024
Monitoring students' academic progress is vital for ensuring timely completion of their studies and supporting at-risk students. Educational Data Mining (EDM) utilizes machine learning and feature selection to gain insights into student performance. However, many feature selection algorithms lack performance forecasting systems, limiting their…
Descriptors: Algorithms, Decision Making, At Risk Students, Learning Management Systems
Iouri Kotorov; Yuliya Krasylnykova; Mar Pérez-Sanagustín; Fernanda Mansilla; Julien Broisin – Journal of Learning Analytics, 2024
The quality of the data and the amount of correct information available is key to informed decision-making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision-making to promote innovation in teaching and learning. In this…
Descriptors: Decision Making, Case Studies, Instructional Innovation, Teaching Methods
Denis Zhidkikh; Ville Heilala; Charlotte Van Petegem; Peter Dawyndt; Miitta Jarvinen; Sami Viitanen; Bram De Wever; Bart Mesuere; Vesa Lappalainen; Lauri Kettunen; Raija Hämäläinen – Journal of Learning Analytics, 2024
Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first…
Descriptors: Learning Analytics, Prediction, School Holding Power, Academic Achievement
Paul Joseph-Richard; James Uhomoibhi – INFORMS Transactions on Education, 2024
Scholarly interests in developing personalized learning analytics dashboards (LADs) in universities have been increasing. LADs are data visualization tools for both teachers and learners that allow them to support student success and improve teaching and learning. In most LADs, however, a teacher-centric, institutional view drives their designs,…
Descriptors: Learning Analytics, Learning Management Systems, Independent Study, Undergraduate Students
Marli Crabtree; Kenneth L. Thompson; Ellen M. Robertson – HAPS Educator, 2024
Research has suggested that changing one's answer on multiple-choice examinations is more likely to lead to positive academic outcomes. This study aimed to further understand the relationship between changing answer selections and item attributes, student performance, and time within a population of 158 first-year medical students enrolled in a…
Descriptors: Anatomy, Science Tests, Medical Students, Medical Education
Marek Hatala; Sina Nazeri – Journal of Learning Analytics, 2024
An essential part of making dashboards more effective in motivating students and leading to desirable behavioural change is knowing what information to communicate to the student and how to frame and present it. Most of the research studying dashboards' impact on learning analyzes learning indicators of students as a group. Understanding how a…
Descriptors: Educational Technology, Information Dissemination, Learning Processes, Algorithms
Rebecka Rundquist; Kristina Holmberg; John Rack; Zeynab Mohseni; Italo Masiello – Journal of Learning Analytics, 2024
The generation, use, and analysis of educational data comes with many promises and opportunities, especially where digital materials allow usage of learning analytics (LA) as a tool in data-based decision-making (DBDM). However, there are questions about the interplay between teachers, students, context, and technology. Therefore, this paper…
Descriptors: Learning Analytics, Elementary Secondary Education, Mathematics Education, Data Analysis
Rosemary Vellar; Boris Handal; Sean Kearney; Chris Forlin – Issues in Educational Research, 2024
Evidence based decision making is essential for enabling improved student learning. Teacher motivations and beliefs about the types and use of data are critical determinants of decision making. Our research explored the types of data teachers use and consider valuable when measuring improvement in student learning. Findings from 294 teachers from…
Descriptors: Catholic Schools, Elementary Secondary Education, Learning Analytics, Student Needs
Yuqin Yang; Carol K. K. Chan; Gaoxia Zhu; Yuyao Tong; Daner Sun – International Journal of Computer-Supported Collaborative Learning, 2024
Knowledge building (KB) competencies are crucial for undergraduates' creative knowledge work and academic success. While there is substantial research on KB discourse, there are limited efforts in examining how KB competencies in the conceptual, metacognitive, socio-emotional, and epistemic dimensions are demonstrated in KB discourse and how the…
Descriptors: Knowledge Level, Scaffolding (Teaching Technique), Undergraduate Students, Reflection
Liu, Kai; Tatinati, Sivanagaraja; Khong, Andy W. H. – IEEE Transactions on Learning Technologies, 2020
Activity-centric data gather feedback on students' learning to enhance learning effectiveness. The heterogeneity and multigranularity of such data require existing data models to perform complex on-the-fly computation when responding to queries of specific granularity. This, in turn, results in latency. In addition, existing data models are…
Descriptors: Context Effect, Models, Learning Analytics, Data Use