期刊名称:Australasian Journal of Educational Technology
印刷版ISSN:1449-3098
电子版ISSN:1449-5554
出版年度:2016
卷号:32
期号:6
DOI:10.14742/ajet.3058
语种:English
出版社:Australasian Society for Computers in Learning in Tertiary Education
摘要:Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students’ decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning science that explain how students learn. We present six learning analytics that reflect what is known in six areas (we call them cases) of theory and research findings in the learning sciences: setting goals and monitoring progress, distributed practice, retrieval practice, prior knowledge for reading, comparative evaluation of writing, and collaborative learning. Our designs demonstrate learning analytics can be grounded in research on self-regulated learning and self-determination. We propose designs for learning analytics in general should guide students toward more effective self-regulated learning and promote motivation through perceptions of autonomy, competence, and relatedness.