Self-evaluation in advanced power searching and mapping with google MOOCs

Julia Wilkowski, D. Russell, Amit Deutsch
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引用次数: 19

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.
基于google mooc的高级电力搜索和地图的自我评价
虽然在创建自动评分的大规模在线开放课程(MOOCs)方面有大量工作要做,但一些复杂的定性考试问题仍然超出了目前的技术水平。对于需要处理这些问题的mooc来说,一个小的课程人员是不可能给学生的定性作业打分的。为了测试自我评价作为一种复杂问题评价方法的有效性,两个谷歌mooc的学生提交了项目并对自己的作业进行了评价。在这两门课程中,助教都会随机抽取一些论文样本进行评分,并将他们的成绩与学生的自我评估成绩进行比较。我们发现许多提交的项目质量非常高,并且大部分自我评估的项目都得到了准确的评估,得分在金标准评分的几分之内。
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