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- W2114519099 abstract "Digital systems underlie a wide range of teaching and learning activities in higher education today. The scope and reach of digital systems now increasingly extend to activities as they occur even inside lecture halls, classrooms, and informal study areas. Learning management systems, interactive student response systems, lecture capture systems, and digitally controlled smart classrooms are examples of technology trends that bring along with them an unprecedented amount of instrumentation quietly collecting lots of data about teacher and learner activities in and across these various spaces. In snapshots, these usage streams offer data that can be helpful for understanding and supporting a particular service. If combined across time and location, the varied data sources potentially open windows onto even more interesting activity patterns and relations. These mosaics, however, can be somewhat difficult to analyze due to the dimensionality of the combined data. Matrix techniques can ease the difficulties of exploring and discovering user activity patterns in such situations. This paper surveys commonly implemented matrix techniques that can be used to enable data mining of user activity information when temporal and spatial data sets are mixed and matched from varied sources." @default.
- W2114519099 created "2016-06-24" @default.
- W2114519099 creator A5018954913 @default.
- W2114519099 date "2013-01-01" @default.
- W2114519099 modified "2023-09-27" @default.
- W2114519099 title "Mixing and matching usage data" @default.
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- W2114519099 doi "https://doi.org/10.1145/2504776.2504806" @default.
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