On generalizability of MOOC models

The big data imposes the key problem of generalizability of the results. In the present contribution, we discuss statistical tools which can help to select variables adequate for target level of abstraction. We show that a model considered as over-fitted in one context can be accurate in another. We illustrate this notion with an example analysis experiment on the data from 13 university Massive Online Open Courses (MOOCs). We discuss statistical tools which can be helpful in the analysis of generalizability of MOOC models.


Published in:
Proceedings of the 9th International Conference on Educational Data Mining, 406-411
Presented at:
9th International Conference on Educational Data Mining, Raleigh, North Carolina, USA., June 29 - July 2, 2016
Year:
2016
Keywords:
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 Record created 2016-12-09, last modified 2018-09-13

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