OU Analyse is a system powered by machine learning methods for early identification of students at risk of failing. All students with their risk of failure in their next assignment are updated weekly and made available to the course tutors and the Student Support Teams to consider appropriate support. The overall objective is to significantly improve the retention of OU students.
How are analytics and machine learning transforming education now, and what is the potential for the future? Hear examples of education leaders who are using analytics & ML to understand student performance and develop new forms of teaching and support. Carnegie Mellon has developed SARA, a socially aware robot tutor, and Unizin and Ivy Tech are using analytics and ML to understand student performance and pilot interventions. Learn how Google is bringing ML to education and organizations.
Through 3 key case studies, Rachel Thomas covers how people can be harmed by machine learning gone wrong, why we as machine learning practitioners should care, and what tech ethics are.
Using deep learning to understand the level of attention and engagement of students. Ensuring privacy, having real-time feedback on the delivery of coursework will help lecturers/presenters, make improvements vs. waiting once or twice a year for this information.
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703-712(августа 2017)Propensity score; Classification trees; Machine learning.
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871-876(августа 2016)Anàlisi de dades; Marginal structural models; Introductori.
Y. Ma, D. Tsao, и H. Shum. (2022)cite arxiv:2207.04630Comment: 24 pages, 11 figures. This updated version makes changes in languages and adds a few additional references. This is the final version to be published.