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.
Feedback improves performance by changing students' locus of attention, focusing them on productive aspects of their work @cite_20 @cite_11 . However, not all peers provide great feedback and some leave limited comments with no coherent message for improvement, or they are rogue reviews @cite_1 . Rogue reviews are insufficient reviews caused by laziness, collusion, dishonesty, ret aliation, competition, or malevolence @cite_23 . To improve on this, PeerStudio @cite_4 peer assessment system is designed to encourage more feedback comments by showing short tips for writing comments just below the comment box. For example, if a response has no constructive feedback, it may remind students with phrases like: ''Quick check: Is your feedback actionable?'' by triggering heuristics word count on feedback @cite_4 . Students see such comments as more useful than rubrics in reviewing @cite_22 . Similar techniques are used to improve the quality of product reviews online @cite_24 . Our framework uses a simple interface design reflecting these lessons by integrating four pointed questions with separate response areas.
@cite_4 @cite_22 @cite_1 @cite_24 @cite_23 @cite_20 @cite_11
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Rapid feedback is a core component of mastery learning, but feedback on open-ended work requires days or weeks in most classes today. This paper introduces PeerStudio, an assessment platform that leverages the large number of students' peers in online classes to enable rapid feedback on in-progress work. Students submit their draft, give rubric-based feedback on two peers' drafts, and then receive peer feedback. Students can integrate the feedback and repeat this process as often as they desire. In MOOC deployments, the median student received feedback in just twenty minutes. Rapid feedback on in-progress work improves course outcomes: in a controlled experiment, students' final grades improved when feedback was delivered quickly, but not if delayed by 24 hours. More than 3,600 students have used PeerStudio in eight classes, both massive and in-person. This research demonstrates how large classes can leverage their scale to encourage mastery through rapid feedback and revision. Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries -- captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8 CIDEr-D improvement after adaptation. Utilizing critics during inference further gives another 4.5 boost. Peer and self-assessment offer an opportunity to scale both assessment and learning to global classrooms. This article reports our experiences with two iterations of the first large online class to use peer and self-assessment. In this class, peer grades correlated highly with staff-assigned grades. The second iteration had 42.9p of students’ grades within 5p of the staff grade, and 65.5p within 10p. On average, students assessed their work 7p higher than staff did. Students also rated peers’ work from their own country 3.6p higher than those from elsewhere. We performed three experiments to improve grading accuracy. We found that giving students feedback about their grading bias increased subsequent accuracy. We introduce short, customizable feedback snippets that cover common issues with assignments, providing students more qualitative peer feedback. Finally, we introduce a data-driven approach that highlights high-variance items for improvement. We find that rubrics that use a parallel sentence structure, unambiguous wording, and well-specified dimensions have lower variance. After revising rubrics, median grading error decreased from 12.4p to 9.9p. Micro-task platforms provide massively parallel, on-demand labor. However, it can be difficult to reliably achieve high-quality work because online workers may behave irresponsibly, misunderstand the task, or lack necessary skills. This paper investigates whether timely, task-specific feedback helps crowd workers learn, persevere, and produce better results. We investigate this question through Shepherd, a feedback system for crowdsourced work. In a between-subjects study with three conditions, crowd workers wrote consumer reviews for six products they own. Participants in the None condition received no immediate feedback, consistent with most current crowdsourcing practices. Participants in the Self-assessment condition judged their own work. Participants in the External assessment condition received expert feedback. Self-assessment alone yielded better overall work than the None condition and helped workers improve over time. External assessment also yielded these benefits. Participants who received external assessment also revised their work more. We conclude by discussing interaction and infrastructure approaches for integrating real-time assessment into online work.