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Liu, Zhichun; Moon, Jewoong – Educational Technology & Society, 2023
In this study, we have proposed and implemented a sequential data analytics (SDA)-driven methodological framework to design adaptivity for digital game-based learning (DGBL). The goal of this framework is to facilitate children's personalized learning experiences for K-5 computing education. Although DGBL experiences can be beneficial, young…
Descriptors: Learning Analytics, Design, Game Based Learning, Computation
Ouyang, Fan; Wu, Mian; Zheng, Luyi; Zhang, Liyin; Jiao, Pengcheng – International Journal of Educational Technology in Higher Education, 2023
As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI…
Descriptors: Technology Integration, Artificial Intelligence, Performance, Prediction
Rets, Irina; Herodotou, Christothea; Bayer, Vaclav; Hlosta, Martin; Rienties, Bart – International Journal of Educational Technology in Higher Education, 2021
Learning analytics dashboards (LADs) can provide learners with insights about their study progress through visualisations of the learner and learning data. Despite their potential usefulness to support learning, very few studies on LADs have considered learners' needs and have engaged learners in the process of design and evaluation. Aligning with…
Descriptors: Learning Analytics, Educational Technology, Usability, College Students

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