Ten challenges in CAD cyber education

Beasley, Zachariah; Piegl, Les A.; Rosen, Paul

doi:10.1080/16864360.2017.1397893

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Seeing Others' Work In engineering education, the use of peer review in the classroom remains an open area of research [2], despite the fact that it is so essential to the design and implementation of engineering systems. For example, Garousi [15] constructed a framework using a "Goal-Question-Metric" (GQM) approach and reported that students gained knowledge in preparing software development documentation and writing code and that students find peer review useful primarily since they can learn from their peers' work.…”

Section: Educational Values Of Peer Reviewmentioning

confidence: 99%

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Leveraging Peer Feedback to Improve Visualization Education

Beasley,

Friedman,

Piegl

et al. 2020

Preprint

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Section: Educational Values Of Peer Reviewmentioning

confidence: 99%

“…We do not include "peer evaluation" of ones own group as peer review 2. Vis Lies: http://www.vislies.org…”

mentioning

confidence: 99%

Leveraging Peer Feedback to Improve Visualization Education

Beasley,

Friedman,

Piegl

et al. 2020

Preprint

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“…Scalable grading of complex assignments (e.g., websites, essays, designs, open-ended questions) is notoriously hard in large, online academic environments [1]- [4]. Peer review is an often-cited solution, but can be unreliable due to a low number of reviewers and an unevaluated review form.…”

Section: Introductionmentioning

confidence: 99%

Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment

Beasley¹,

Piegl²,

Rosen³

2021

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Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has 1) leveraged sentiment analysis in the peer-review process to inform or validate grades or 2) utilized aspect extraction to craft a review form from what students actually communicated. Our work utilizes, rather than discards, student data from review form comments to deliver better information to the instructor. In this work, we detail the process by which we create our domaindependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm which provides a fine-grained sentiment score from text alone. We end by analyzing validity and discussing conclusions from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses.

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