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Kamila Misiejuk; Sonsoles López-Pernas; Rogers Kaliisa; Mohammed Saqr – Journal of Learning Analytics, 2025
Generative artificial intelligence (GenAI) has opened new possibilities for designing learning analytics (LA) tools, gaining new insights about student learning processes and their environment, and supporting teachers in assessing and monitoring students. This systematic literature review maps the empirical research of 41 papers utilizing GenAI…
Descriptors: Literature Reviews, Artificial Intelligence, Learning Analytics, Data Collection
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David Williamson Shaffer; Yeyu Wang; Andrew Ruis – Journal of Learning Analytics, 2025
Learning is a multimodal process, and learning analytics (LA) researchers can readily access rich learning process data from multiple modalities, including audio-video recordings or transcripts of in-person interactions; logfiles and messages from online activities; and biometric measurements such as eye-tracking, movement, and galvanic skin…
Descriptors: Learning Processes, Learning Analytics, Models, Data
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Paul Prinsloo; Mohammad Khalil; Sharon Slade – Journal of Computing in Higher Education, 2024
Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled…
Descriptors: Learning Analytics, Data, Ecology, Models
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Allyson Skene; Laura Winer; Erika Kustra – International Journal for Academic Development, 2024
This article explores potential uses, misuses, beneficiaries, and tensions of learning analytics in higher education. While those promoting and using learning analytics generally agree that ethical practice is imperative, and student privacy and rights are important, navigating the complex maze of ethical dilemmas can be challenging, particularly…
Descriptors: Learning Analytics, Higher Education, Ethics, Privacy
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Mark Nichols – Open Learning, 2024
Learning analytics promise significant benefit to online education providers through improved, better-targeted student services. Much has been written about the potential of analytics and how they might be technically implemented, and various ethical considerations are published highlighting the significant potential risk of gathering,…
Descriptors: Learning Analytics, Ethics, Guidelines, Policy Formation
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Anil Harun Kiliç; Serkan Izmirli – Asian Journal of Distance Education, 2024
This study conducted a systematic literature review of articles on learning analytics published between 2004 and January 2024. A total of 1,064 articles, identified using the keyword "learning analytic*" in the Scopus database, were analyzed. The study integrated systematic literature review and bibliometric analysis approaches to…
Descriptors: Literature Reviews, Learning Analytics, Foreign Countries, Data Use
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Rafael Ferreira Mello; Elyda Freitas; Luciano Cabral; Filipe Dwan Pereira; Luiz Rodrigues; Mladen Rakovic; Jackson Raniel; Dragan Gaševic – Journal of Learning Analytics, 2024
Learning analytics (LA) involves the measurement, collection, analysis, and reporting of data about learners and their contexts, aiming to understand and optimize both the learning process and the environments in which it occurs. Among many themes that the LA community considers, natural language processing (NLP) algorithms have been widely…
Descriptors: Literature Reviews, Learning Analytics, Natural Language Processing, Data Collection
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David P. Reid; Timothy D. Drysdale – IEEE Transactions on Learning Technologies, 2024
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment…
Descriptors: Learning Analytics, Laboratory Experiments, Electronic Learning, Feedback (Response)
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Chen Zhan; Srecko Joksimovic; Djazia Ladjal; Thierry Rakotoarivelo; Ruth Marshall; Abelardo Pardo – IEEE Transactions on Learning Technologies, 2024
Data are fundamental to Learning Analytics (LA) research and practice. However, the ethical use of data, particularly in terms of respecting learners' privacy rights, is a potential barrier that could hinder the widespread adoption of LA in the education industry. Despite the policies and guidelines of privacy protection being available worldwide,…
Descriptors: Privacy, Learning Analytics, Ethics, Data Use
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Yu-Jie Wang; Chang-Lei Gao; Xin-Dong Ye – Education and Information Technologies, 2024
The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and…
Descriptors: Precision Teaching, Data Use, Intervention, Educational Improvement
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John Stamper; Steven Moore; Carolyn P. Rosé; Philip I. Pavlik Jr.; Kenneth Koedinger – Journal of Educational Data Mining, 2024
LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates…
Descriptors: Learning Analytics, Web Sites, Data Use, Educational Technology
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Ana Stojanov; Ben Kei Daniel – Education and Information Technologies, 2024
The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and…
Descriptors: Higher Education, Learning Analytics, Well Being, Decision Making
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Prasoon Patidar; Tricia J. Ngoon; Neeharika Vogety; Nikhil Behari; Chris Harrison; John Zimmerman; Amy Ogan; Yuvraj Agarwal – Journal of Learning Analytics, 2024
Classroom sensing systems can capture data on teacher-student behaviours and interactions at a scale far greater than human observers can. These data, translated to multi-modal analytics, can provide meaningful insights to educational stakeholders. However, complex data can be difficult to make sense of. In addition, analyses done on these data…
Descriptors: Learning Analytics, Classroom Observation Techniques, Data Analysis, Student Behavior
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Oleksandra Poquet – British Journal of Educational Technology, 2024
The paper argues that learning analytics as a research field can benefit from a theory-informed shared language to describe sensemaking of learning and teaching data. To make the case for such shared language, first, I critically review prominent sensemaking theories to then demonstrate how studies in learning analytics do not use coherent…
Descriptors: Learning Analytics, Data, Affordances, Theories
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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
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