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Kitto, Kirsty; Lupton, Mandy; Davis, Kate; Waters, Zak – Australasian Journal of Educational Technology, 2017
Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such as sense-making,…
Descriptors: Curriculum Design, Design Requirements, Student Records, Data
Liu, Min; Kang, Jina; Zou, Wenting; Lee, Hyeyeon; Pan, Zilong; Corliss, Stephanie – Technology, Knowledge and Learning, 2017
There is much enthusiasm in higher education about the benefits of adaptive learning and using big data to investigate learning processes to make data-informed educational decisions. The benefits of adaptive learning to achieve personalized learning are obvious. Yet, there lacks evidence-based research to understand how data such as user behavior…
Descriptors: College Freshmen, Pharmaceutical Education, Individualized Instruction, Data
Kutsyuruba, Benjamin; Christou, Theodore; Heggie, Lindsay; Murray, James; Deluca, Christopher – Canadian Journal of Educational Administration and Policy, 2015
Collaborative inquiry (CI) has emerged as a dominant structure for educators' professional learning in the 21st century. The purpose of this paper is to analyze publicly available documents and policies related to CI in Ontario in order to better understand the documentary scope and spread of this professional learning model in the province. We…
Descriptors: Foreign Countries, Teacher Collaboration, Inquiry, Educational Policy

Cowley-Durst, Barbara – Performance Improvement, 1999
Discusses knowledge management that seeks to minimize information overload in order to enhance performance. Highlights include the differences between data, information, and knowledge; the relationship between learning, knowledge, and performance; the use of focus groups; documenting results; and knowledge classification. (LRW)
Descriptors: Classification, Data, Focus Groups, Information Utilization