Improving Assessment on MOOCs Through Peer Identification and Aligned Incentives

Dilrukshi Gamage; Haritha Thilakarathne; Indika Perera; Mark Whiting; Shantha Fernando; Thejan Rajapakshe

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Improving Assessment on MOOCs Through Peer Identification and Aligned Incentives

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arxiv 1703.06169 v1 pith:S7DZLURC submitted 2017-03-17 cs.CY cs.HC

Dilrukshi Gamage , Mark Whiting , Thejan Rajapakshe , Haritha Thilakarathne , Indika Perera , Shantha Fernando This is my paper

classification cs.CYcs.HC

keywords peerassessmentfeedbackmoocsincentivesopenqualityaligned

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Massive Open Online Courses (MOOCs) use peer assessment to grade open ended questions at scale, allowing students to provide feedback. Relative to teacher based grading, peer assessment on MOOCs traditionally delivers lower quality feedback and fewer learner interactions. We present the identified peer review (IPR) framework, which provides non-blind peer assessment and incentives driving high quality feedback. We show that, compared to traditional peer assessment methods, IPR leads to significantly longer and more useful feedback as well as more discussion between peers.

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