Title

Using Large-scale LMS Data Portal Data to Improve Teaching and Learning (at K-State)

Location

Kansas State University-Manhattan Campus

Session Type

Poster

Session Abstract

With any learning management system, a byproduct of its function is data, which may be analyzed to improve awareness, decision-making, and actions. At Kansas State University, its Canvas LMS instance recently made available its cumulative data from its first use in 2013. These flat files open a window to how the university is harnessing its LMS, with some macro-level insights that may suggest some areas to improve teaching and learning. This session describes some approaches to informatizing this empirical “big data” with some basic approaches: reviewing the data dictionary, extracting basic descriptions of the respective data sets, conducting time-based comparisons, surfacing testable hypotheses from data inferences, and conducting other data explorations. This introduces initial data analysis work only, but this does not preclude front-end analysis of courses at the micro level, relational database queries of the data, and other potential follow-on work.

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Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

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Using Large-scale LMS Data Portal Data to Improve Teaching and Learning (at K-State)

Kansas State University-Manhattan Campus

With any learning management system, a byproduct of its function is data, which may be analyzed to improve awareness, decision-making, and actions. At Kansas State University, its Canvas LMS instance recently made available its cumulative data from its first use in 2013. These flat files open a window to how the university is harnessing its LMS, with some macro-level insights that may suggest some areas to improve teaching and learning. This session describes some approaches to informatizing this empirical “big data” with some basic approaches: reviewing the data dictionary, extracting basic descriptions of the respective data sets, conducting time-based comparisons, surfacing testable hypotheses from data inferences, and conducting other data explorations. This introduces initial data analysis work only, but this does not preclude front-end analysis of courses at the micro level, relational database queries of the data, and other potential follow-on work.

https://newprairiepress.org/isitl/2017/Posters/7