Self-evaluation in Advanced Power Searching and Mapping with Google MOOCs
Venue
ACM Learning at Scale (2014)
Publication Year
2014
Authors
Julia Wilkowski, Daniel M. Russell, Amit Deutsch
BibTeX
Abstract
While there is a large amount of work on creating autograded massive open online
courses (MOOCs), some kinds of complex, qualitative exam questions are still beyond
the current state of the art. For MOOCs that need to deal with these kinds of
questions, it is not possible for a small course staff to grade students’
qualitative work. To test the efficacy of self-evaluation as a method for
complex-question evaluation, students in two Google MOOCs have submitted projects
and evaluated their own work. For both courses, teaching assistants graded a random
sample of papers and compared their grades with self-evaluated student grades. We
found that many of the submitted projects were of very high quality, and that a
large majority of self-evaluated projects were accurately evaluated, scoring within
just a few points of the gold standard grading.
