BayesRank

Waters, Andrew E.; Tinapple, David; Baraniuk, Richard G.

doi:10.1145/2724660.2724672

Cited by 17 publications

(3 citation statements)

References 15 publications

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“…However, prior work on ordinal grading centers around the theoretical viability of accurately ranking submissions (e.g. [23,38,45,51]); Juxtapeer instead emphasizes comparison as a scaffold to help reviewers provide better feedback. Perhaps most similarly, the ComPAIR system shows two peer submissions side-by-side.…”

Section: Related Work In Peer Reviewmentioning

confidence: 99%

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Juxtapeer

Cambre

Klemmer

Kulkarni

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Section: Related Work In Peer Reviewmentioning

confidence: 99%

“…For example, Raman & Joachims [38] use 10-point Likert ratings. With BayesRank [51], learners rank a small set of submissions (e.g. from 1 to 5).…”

Section: Study 2: Can Comparative Review Reliably Rank Submission Quamentioning

confidence: 99%

Juxtapeer

Cambre

Klemmer

Kulkarni

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…Other instructors have augmented their peer assessment systems with algorithmic weighting of peer grades [3,4,14] and peer-matching procedures based on machine learning [15] to further improve accuracy. (Our own peer assessment system included one such algorithmic enhancement, but we exclude it from our current analysis in favor of looking only at the students' raw responses.…”

Section: Introductionmentioning

confidence: 99%

Peer assessment of student-produced mechanics lab report videos

Douglas

Aiken

Lin

et al. 2017

Phys. Rev. Phys. Educ. Res.

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We examine changes in students' rating behavior during a semester-long sequence of peer evaluation laboratory exercises in an introductory mechanics course. We perform a quantitative analysis of the ratings given by students to peers' physics lab reports, and conduct interviews with students. We find that peers persistently assign higher ratings to lab reports than do experts, that peers begin the semester by giving high ratings most frequently and end the semester with frequent middle ratings, and that peers go through the semester without much change in the frequency of low ratings. We then use student interviews to develop a model for student engagement with peer assessment. This model is based on two competing influences which appear to shape peer evaluation behavior: a strong disinclination to give poor ratings with a complementary preference to give high ratings when in doubt, and an attempt to develop an expertlike criticality when assessing peers' work.

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