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da Silva, Lidia M.; Dias, Lucas P. S.; Barbosa, Jorge L. V.; Rigo, Sandro J.; dos Anjos, Julio C. S.; Geyer, Claudio F. R.; Leithardt, Valderi R. Q. – Informatics in Education, 2022
Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist…
Descriptors: Learning Analytics, Cooperative Learning, Distance Education, Electronic Learning
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Keskin, Sinan; Yurdugül, Halil – Journal of Educational Technology and Online Learning, 2022
This study aims to examine e-learning experiences of the learners by using learner system interaction metrics. In this context, an e-learning environment has been structured within the scope of a course. Learners interacted with learning activities and leave various traces when they interact with others, contents, and assessment tasks. Log data…
Descriptors: Electronic Learning, Learning Experience, Models, Learning Activities
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Hellings, Jan; Haelermans, Carla – Higher Education: The International Journal of Higher Education Research, 2022
We use a randomised experiment to study the effect of offering half of 556 freshman students a learning analytics dashboard and a weekly email with a link to their dashboard, on student behaviour in the online environment and final exam performance. The dashboard shows their online progress in the learning management systems, their predicted…
Descriptors: Learning Analytics, College Freshmen, Student Behavior, Electronic Learning
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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)
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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
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Kaliisa, Rogers; Dolonen, Jan Arild – Technology, Knowledge and Learning, 2023
Despite the potential of learning analytics (LA) to support teachers' everyday practice, its adoption has not been fully embraced due to the limited involvement of teachers as co-designers of LA systems and interventions. This is the focus of the study described in this paper. Following a design-based research (DBR) approach and guided by concepts…
Descriptors: College Faculty, Student Participation, Discourse Analysis, Behavior Patterns
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Kim, Hodam; Chae, Younsoo; Kim, Suhye; Im, Chang-Hwan – IEEE Transactions on Learning Technologies, 2023
Owing to the rapid development of information and communication technologies, online or mobile learning content is widely available on the Internet. Unlike traditional face-to-face learning, online learning exhibits a critical limitation: real-time interactions between learners and teachers are generally not feasible in online learning. To…
Descriptors: College Students, Control Groups, Attention, Comprehension
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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
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Piet Kommers Ed.; Inmaculada Arnedillo Sánchez Ed.; Pedro Isaías Ed. – International Association for Development of the Information Society, 2023
These proceedings contain the papers and posters of the 21st International Conference on e-Society (ES 2023) and 19th International Conference on Mobile Learning (ML 2023), organised by the International Association for Development of the Information Society (IADIS) in Lisbon, Portugal, during March 11-13, 2023. The e-Society 2023 conference aims…
Descriptors: Electronic Learning, Educational Technology, Telecommunications, Handheld Devices
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Yang, Christopher C. Y.; Chen, Irene Y. L.; Ogata, Hiroaki – Educational Technology & Society, 2021
Precision education is now recognized as a new challenge of applying artificial intelligence, machine learning, and learning analytics to improve both learning performance and teaching quality. To promote precision education, digital learning platforms have been widely used to collect educational records of students' behavior, performance, and…
Descriptors: Learning Analytics, Individualized Instruction, Instructional Materials, Books
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Sahin, Muhittin; Ulucan, Aydin; Yurdugül, Halil – Education and Information Technologies, 2021
E-learning environments can store huge amounts of data on the interaction of learners with the content, assessment and discussion. Yet, after the identification of meaningful patterns or learning behaviour in the data, it is necessary to use these patterns to improve learning environments. It is notable that designs to benefit from these patterns…
Descriptors: Electronic Learning, Data Collection, Decision Making, Evaluation Criteria
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Zhao, Fuzheng; Hwang, Gwo-Jen; Yin, Chengjiu – Educational Technology & Society, 2021
Educational data mining and learning analytics have become a very important topic in the field of education technology. Many frameworks have been proposed for learning analytics which make it possible to identify learning behavior patterns or strategies. However, it is difficult to understand the reason why behavior patterns occur and why certain…
Descriptors: Behavior Patterns, Reading, Textbooks, Electronic Learning
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Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
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Brown, Alice; Lawrence, Jill; Basson, Marita; Redmond, Petrea – Higher Education Research and Development, 2022
Student engagement is consistently identified as a key predictor of learner outcomes within the online learning environment. However, there is limited guidance about using proactive strategies to improve engagement for low and non-engaged students: for example by specifically employing course learning analytics (CLA) and nudging strategies in…
Descriptors: Electronic Learning, Learner Engagement, Instructional Improvement, College Instruction
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Luis, Ricardo M. Meira Ferrão; Llamas-Nistal, Martin; Iglesias, Manuel J. Fernández – Smart Learning Environments, 2022
E-learning students have a tendency to get demotivated and easily dropout from online courses. Refining the learners' involvement and reducing dropout rates in these e-learning based scenarios is the main drive of this study. This study also shares the results obtained and crafts a comparison with new and emerging commercial solutions. In a…
Descriptors: Artificial Intelligence, Identification, Electronic Learning, Dropout Characteristics
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