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Lixiang Yan; Vanessa Echeverria; Yueqiao Jin; Gloria Fernandez-Nieto; Linxuan Zhao; Xinyu Li; Riordan Alfredo; Zachari Swiecki; Dragan Gaševic; Roberto Martinez-Maldonado – British Journal of Educational Technology, 2024
Multimodal learning analytics (MMLA) offers the potential to provide evidence-based insights into complex learning phenomena such as collaborative learning. Yet, few MMLA applications have closed the learning analytics loop by being evaluated in real-world educational settings. This study evaluates the effectiveness of an MMLA solution in…
Descriptors: Learning Analytics, Cooperative Learning, Longitudinal Studies, Allied Health Occupations Education
Lazrig, Ibrahim; Humpherys, Sean L. – Information Systems Education Journal, 2022
Can sentiment analysis be used in an educational context to help teachers and researchers evaluate students' learning experiences? Are sentiment analyzing algorithms accurate enough to replace multiple human raters in educational research? A dataset of 333 students evaluating a learning experience was acquired with positive, negative, and neutral…
Descriptors: College Students, Learning Analytics, Educational Research, Learning Experience
Alzahrani, Asma Shannan; Tsai, Yi-Shan; Aljohani, Naif; Whitelock-wainwright, Emma; Gasevic, Dragan – Educational Technology Research and Development, 2023
Learning analytics (LA) has gained increasing attention for its potential to improve different educational aspects (e.g., students' performance and teaching practice). The existing literature identified some factors that are associated with the adoption of LA in higher education, such as stakeholder engagement and transparency in data use. The…
Descriptors: Teacher Attitudes, Trust (Psychology), Learning Analytics, Higher Education
Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence

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