Assessment Analytics for Peer-Assessment: A Model and Implementation

نویسندگان

  • Blazenka Divjak
  • Darko Grabar
  • Marcel Maretic
چکیده

Learning analytics should go beyond data analysis and include approaches and algorithms that are meaningful for learner performance and that can be interpreted by teacher and related to learning outcomes. Assessment analytics has been lagging behind other research in learning analytics. This holds true especially for peer-assessment analytics. In this paper we present a mathematical model for peerassessment based on the use of scoring rubrics for criteriabased assessment. We propose methods for the calculation of the final grade along with reliability measurues of peerassessment. Modeling is motivated and driven by the identified peer-assessment scenarios. Use of peer-assessment based on a sound model provides benefits of the deeper learning while addressing the issues of validity and reliability.

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تاریخ انتشار 2016