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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
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
Flavio Manganello; Chiara Fante – Journal of Learning Analytics, 2025
Learning Analytics (LA) is increasingly applied to assess and foster creativity in educational settings. Whereas existing applications have shown promise in STEM contexts, less is known about the diversity of approaches across educational domains. Therefore, we conducted a scoping review that systematically mapped LA applications for creativity in…
Descriptors: Literature Reviews, Creativity, Context Effect, Learning Analytics
Manel van Kessel; Inge Molenaar; Carolien A. N. Knoop-van Campen; Mario de Jonge; Nadira Saab – Journal of Learning Analytics, 2025
Adaptive learning technologies (ALTs) provide teachers with student data in teacher dashboards (TDs). However, there is substantial variation in dashboard use among teachers, and many find it difficult to draw conclusions based on student data. Teachers' skills, knowledge, and contextual conditions are believed to be essential in effective…
Descriptors: Elementary School Teachers, Technology Uses in Education, Teacher Attitudes, Data Collection
Chaewon Lee; Lan Luo; Shelbi L. Kuhlmann; Robert D. Plumley; Abigail T. Panter; Matthew L. Bernacki; Jeffrey A. Greene; Kathleen M. Gates – Journal of Learning Analytics, 2025
The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…
Descriptors: Electronic Learning, Prediction, Data Analysis, Artificial Intelligence
Ridwan Whitehead; Andy Nguyen; Sanna Järvelä – Journal of Learning Analytics, 2025
Incorporating non-verbal data streams is essential to understanding the dynamics of interaction within collaborative learning environments in which a variety of verbal and non-verbal modes of communication intersect. However, the complexity of non-verbal data -- especially gathered in the wild from collaborative learning contexts -- demands…
Descriptors: Case Studies, Nonverbal Communication, Video Technology, Data Analysis
Xavier Ochoa; Xiaomeng Huang; Yuli Shao – Journal of Learning Analytics, 2025
Generative AI (GenAI) has the potential to revolutionize the analysis of educational data, significantly impacting learning analytics (LA). This study explores the capability of non-experts, including administrators, instructors, and students, to effectively use GenAI for descriptive LA tasks without requiring specialized knowledge in data…
Descriptors: Learning Analytics, Artificial Intelligence, Computer Software, Scores
Xiner Liu; Andres Felipe Zambrano; Ryan S. Baker; Amanda Barany; Jaclyn Ocumpaugh; Jiayi Zhang; Maciej Pankiewicz; Nidhi Nasiar; Zhanlan Wei – Journal of Learning Analytics, 2025
This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…
Descriptors: Coding, Artificial Intelligence, Automation, Data Analysis

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