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Jessica D. Young; Lisa Dawood; Scott E. Lewis – Journal of Chemical Education, 2024
Instructors use of Artificial Intelligence (AI) language models (i.e., chatbots) as an educational resource will require an understanding of students' AI literacy, namely their ability to critically reflect on the relevance, trustworthiness, and quality of these tools in the context of chemistry. This study sought to describe students' AI literacy…
Descriptors: Chemistry, Science Instruction, Artificial Intelligence, Multiple Literacies
Yueqiao Jin; Vanessa Echeverria; Lixiang Yan; Linxuan Zhao; Riordan Alfredo; Yi-Shan Tsai; Dragan Gasevic; Roberto Martinez-Maldonado – Journal of Learning Analytics, 2024
Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across…
Descriptors: Learning Analytics, Accountability, Ethics, Artificial Intelligence
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