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Wollny, Sebastian; Di Mitri, Daniele; Jivet, Ioana; Muñoz-Merino, Pedro; Scheffel, Maren; Schneider, Jan; Tsai, Yi-Shan; Whitelock-Wainwright, Alexander; Gaševic, Dragan; Drachsler, Hendrik – Journal of Computer Assisted Learning, 2023
Background: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders'…
Descriptors: Foreign Countries, College Students, Learning Analytics, Expectation
Marijn Martens; Ralf De Wolf; Lieven De Marez – Technology, Knowledge and Learning, 2025
Algorithmic decision-making systems such as Learning Analytics (LA) are widely used in an educational setting ranging from kindergarten to university. Most research focuses on how LA is used and adopted by teachers. However, the perspective of students and parents who experience the (in)direct consequences of these systems is underexplored. This…
Descriptors: Algorithms, Decision Making, Learning Analytics, Secondary School Students
Viberg, Olga; Engström, Linda; Saqr, Mohammed; Hrastinski, Stefan – Education and Information Technologies, 2022
In order to successfully implement learning analytics (LA), we need a better understanding of student expectations of such services. Yet, there is still a limited body of research about students' expectations across countries. Student expectations of LA have been predominantly examined from a view that perceives students as a group of individuals…
Descriptors: Learning Analytics, Student Attitudes, Expectation, College Students
Anni Silvola; Amanda Sjöblom; Piia Näykki; Egle Gedrimiene; Hanni Muukkonen – Frontline Learning Research, 2023
An in-depth understanding of student experiences and evaluations of learning analytics dashboards (LADs) is needed to develop supportive learning analytics tools. This study investigates how students (N = 140) evaluated two student-facing LADs as a support for academic path-level self-regulated learning (SRL) through the concrete processes of…
Descriptors: Learning Analytics, Student Evaluation, Student Experience, Student Attitudes
Nick Hopwood; Tracey-Ann Palmer; Gloria Angela Koh; Mun Yee Lai; Yifei Dong; Sarah Loch; Kun Yu – International Journal of Research & Method in Education, 2025
Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in…
Descriptors: Psychological Patterns, Student Evaluation, Learning Analytics, Learner Engagement
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
Stanislav Pozdniakov; Jonathan Brazil; Mehrnoush Mohammadi; Mollie Dollinger; Shazia Sadiq; Hassan Khosravi – Journal of Learning Analytics, 2025
Engaging students in creating high-quality novel content, such as educational resources, promotes deep and higher-order learning. However, students often lack the necessary training or knowledge to produce such content. To address this gap, this paper explores the potential of incorporating generative AI (GenAI) to review students' work and…
Descriptors: Student Evaluation, Artificial Intelligence, Student Developed Materials, Feedback (Response)
Damien S. Fleur; Max Marshall; Miguel Pieters; Natasa Brouwer; Gerrit Oomens; Angelos Konstantinidis; Koos Winnips; Sylvia Moes; Wouter van den Bos; Bert Bredeweg; Erwin A. van Vliet – Journal of Learning Analytics, 2023
Personalized feedback is important for the learning process, but it is time consuming and particularly problematic in large-scale courses. While automatic feedback may help for self-regulated learning, not all forms of feedback are effective. Social comparison offers powerful feedback but is often loosely designed. We propose that intertwining…
Descriptors: Feedback (Response), Peer Influence, Learning Analytics, Undergraduate Students
Shihui Feng; David Gibson; Dragan Gaševic – Journal of Learning Analytics, 2025
Understanding students' emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Student Role, Learning Analytics
Tomás Bautista-Godínez; Gerardo Castañeda-Garza; Ricardo Pérez Mora; Hector G. Ceballos; Verónica Luna de la Luz; J. Gerardo Moreno-Salinas; Irma Rocío Zavala-Sierra; Roberto Santos-Solórzano; Carlos Iván Moreno Arellano; Melchor Sánchez-Mendiola – Journal of Learning Analytics, 2024
The adoption of learning analytics (LA) in higher education institutions (HEIs) in Mexico is still at an early stage despite increasing global interest and advances in the field. The use of educational data remains a challenging puzzle for many universities, which strive to provide students, teachers, and institutional administrators with…
Descriptors: Foreign Countries, Learning Analytics, Universities, Program Implementation
Haruna Abe; Kay Colthorpe; Pedro Isaias – Discover Education, 2025
To improve the online learning experience, adaptive learning technologies are being used to personalise learning content to suit individual learning needs, with learning analytics being integrated to collect data about the student usage behaviour on the platform. Research indicates that the adaptive learning platforms promote a supportive learning…
Descriptors: Physiology, Science Instruction, Instructional Design, Learning Management Systems
Gomathy Ramaswami; Teo Susnjak; Anuradha Mathrani – Journal of Learning Analytics, 2023
Learning Analytics Dashboards (LADs) are gaining popularity as a platform for providing students with insights into their learning behaviour patterns in online environments. Existing LAD studies are mainly centred on displaying students' online behaviours with simplistic descriptive insights. Only a few studies have integrated predictive…
Descriptors: Learner Engagement, Learning Analytics, Electronic Learning, Student Behavior
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
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)
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