Instant feedback for increasing the presence of solutions in peer reviews

NAACL-HLT 2016 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session

Volumn , Issue , 2016, Pages 6-10

  Nguyen, Huy ^a   Xiong, Wenting ^c   Litman, Diane ^a,b  

^a Learning Research and Development Ctr   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c IBM WATSON HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS;

DESIGN AND EVALUATIONS; HIGH SCHOOL CLASSROOMS; HIGH-PRECISION; PEER REVIEW; PEER REVIEW SYSTEM; PREDICTION MODELLING; STUDENT REVIEWERS; SYSTEM DEPLOYMENT; SYSTEM FEEDBACKS; WEB BASED;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 85016508799     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent Dirichlet allocation. The Journal of Machine Learning Research, 3:993-1022.
2. Kwangsu Cho and Christian D. Schunn. 2007. Scaffolded Writing and Rewriting in the Discipline: A Web-based Reciprocal Peer Review System. Computers & Education, 48(3):409-426, April.
3. Andrea Ernst-Gerlach and Gregory Crane. 2008. Identifying Quotations in Reference Works and Primary Materials. In Research and Advanced Technology for Digital Libraries, volume 5173 of Lecture Notes in Computer Science, pages 78-87. Springer Berlin Heidelberg.
4. Sarah Gielen, Elien Peeters, Filip Dochy, Patrick Onghena, and Katrien Struyven. 2010. Improving the effectiveness of peer feedback for learning. Learning and Instruction, 20(4):304 - 315. Unravelling Peer Assessment.
5. Soo-Min Kim, Patrick Pantel, Tim Chklovski, and Marco Pennacchiotti. 2006. Automatically Assessing Review Helpfulness. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP '06, pages 423-430, Stroudsburg, PA, USA. Association for Computational Linguistics.
6. Kristi Lundstrom andWendy Baker. 2009. To give is better than to receive: The benefits of peer review to the reviewer's own writing. Journal of Second Language Writing, 18(1):30-43, March.
7. Melissa Nelson and Christian Schunn. 2009. The nature of feedback: How different types of peer feedback affect writing performance. Instructional Science, 37(4):375-401.
8. Huy Nguyen and Diane Litman. 2013. Identifying Localization in Peer Reviews of Argument Diagrams. In Artificial Intelligence in Education, volume 7926 of Lecture Notes in Computer Science, pages 91-100. Springer Berlin Heidelberg.
9. Huy Nguyen and Diane Litman. 2014. Improving Peer Feedback Prediction: The Sentence Level is Right. In Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications, pages 99-108, Baltimore, Maryland, June. Association for Computational Linguistics.
10. Huy Nguyen, Wenting Xiong, and Diane Litman. 2014. Classroom Evaluation of a Scaffolding Intervention for Improving Peer Review Localization. In Intelligent Tutoring Systems, volume 8474 of Lecture Notes in Computer Science, pages 272-282. Springer International Publishing.
11. Lakshmi Ramachandran and Edward F. Gehringer. 2013. A User Study on the Automated Assessment of Reviews. In Proceedings of theWorkshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013, Memphis, USA, July 9-13, 2013.
12. Lakshmi Ramachandran and Edward F. Gehringer. 2015. Identifying Content Patterns in Peer Reviews Using Graph-based Cohesion. In Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015, Hollywood, Florida. May 18-20, 2015., pages 269-275.
13. Wenting Xiong and Diane Litman. 2010. Identifying Problem Localization in Peer-Review Feedback. In Intelligent Tutoring Systems, volume 6095 of Lecture Notes in Computer Science, pages 429-431. Springer Berlin Heidelberg.
14. Wenting Xiong, Diane J Litman, and Christian D Schunn. 2010. Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data. ERIC.
15. Wenting Xiong, Diane Litman, and Christian Schunn. 2012. Natural Language Processing techniques for researching and improving peer feedback. Journal of Writing Research, 4(2):155-176. Query date: 2015-05-24.