Difference between revisions of "Research Goals"
(New page: == Researcher Types == # cognitive psychologist # educational psychologist # course developer # user modeling researcher # ITS/AIED researcher # data miner/computer scientist # psychomet...) |
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# test a theory of performance or learning | # test a theory of performance or learning | ||
| − | If, for example, you want to test whether a power law or exponential function better fits learning data, you might use DataShop data sets to do so as follows. You might export data from a dataset, e.g. Geometry Area, 1996-1997. and open it into a software package like Matlab or R, and use programs for modeling, such as generalized linear regression, to compare alternate versions of your theory. You can find instructions on how to read an exported file into R here. | + | If, for example, you want to test whether a power law or exponential function better fits learning data, you might use DataShop data sets to do so as follows. You might export data from a dataset, e.g. [https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=76 Geometry Area, 1996-1997]. and open it into a software package like Matlab or R, and use programs for modeling, such as generalized linear regression, to compare alternate versions of your theory. You can find instructions on how to read an exported file into R here. |
# see the shape of a learning curve | # see the shape of a learning curve | ||
Latest revision as of 14:52, 22 August 2013
Researcher Types
- cognitive psychologist
- educational psychologist
- course developer
- user modeling researcher
- ITS/AIED researcher
- data miner/computer scientist
- psychometrician
- learning analytics researcher
Research Goals
- test a theory of performance or learning
If, for example, you want to test whether a power law or exponential function better fits learning data, you might use DataShop data sets to do so as follows. You might export data from a dataset, e.g. Geometry Area, 1996-1997. and open it into a software package like Matlab or R, and use programs for modeling, such as generalized linear regression, to compare alternate versions of your theory. You can find instructions on how to read an exported file into R here.
- see the shape of a learning curve
A learning curve visualizes changes in student performance over time. In DataShop, it is typical to view such a curve aggregated over data for many students on many problems (though you can view as much or as little in the aggregate as you'd like). A good learning curve reveals improvement in student performance as opportunity count (practice with a given knowledge component, or skill) increases. See a visual explanation of a learning curve, view some learning curve examples (requires login), or watch a video on how to interpret learning curves.
- analyze my ed. tech. data to find ways to improve student learning
You can import your data into DataShop and use DataShop tools to find difficulty factors in your course, for instance,
