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Lee, Ji-Eun; Chan, Jenny Yun-Chen; Botelho, Anthony; Ottmar, Erin – Educational Technology Research and Development, 2022
Online educational games have been widely used to support students' mathematics learning. However, their effects largely depend on student-related factors, the most prominent being their behavioral characteristics as they play the games. In this study, we applied a set of learning analytics methods (k-means clustering, data visualization) to…
Descriptors: Computer Games, Educational Games, Mathematics Instruction, Learning Processes
Valle, Natercia; Antonenko, Pavlo; Valle, Denis; Sommer, Max; Huggins-Manley, Anne Corinne; Dawson, Kara; Kim, Dongho; Baiser, Benjamin – Educational Technology Research and Development, 2021
Based on the achievement goal theory, this experimental study explored the influence of predictive and descriptive learning analytics dashboards on graduate students' motivation and statistics anxiety in an online graduate-level statistics course. Participants were randomly assigned into one of three groups: (1) predictive dashboard; (2)…
Descriptors: Online Courses, Graduate Students, Statistics Education, Anxiety
Cohen, Anat; Ezra, Orit; Hershkovitz, Arnon; Tzayada, Odelia; Tabach, Michal; Levy, Ben; Segal, Avi; Gal, Kobi – Educational Technology Research and Development, 2021
Personalizing the use of educational mathematics applets to fit learners' characteristics poses a great challenge. The present study adopted a unique approach by comparing personalization processes implemented by a machine to those implemented by a human teacher. Given the different affordances--the machine's access to historical log file data,…
Descriptors: Mathematics Instruction, Comparative Analysis, Pedagogical Content Knowledge, Teaching Methods
Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek; Hlosta, Martin – Educational Technology Research and Development, 2019
By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction.…
Descriptors: Prediction, Learning Analytics, Teacher Role, Teacher Attitudes