Abstract
In a controlled experiment using Comrade, a computer-supported peer review system, student reviewers offered feedback to student authors on their written analyses of a problem scenario. In each condition, reviewers received a different type of rating prompt: domain-related writing composition prompts or problem/issue specific prompts. We found that the reviewers were sensitive to the type of rating prompts they saw and that their ratings of authors’ work were less discriminating with respect to writing composition than to problem-specific issues. In other words, when students gave each other feedback regarding domain-relevant writing criteria, their ratings correlated to a much greater extent, suggesting that such ratings are redundant.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Topping, K.: Peer assessment between students in colleges and universities. Review of Educational Research 68, 249–276 (1998)
Cho, K., Schunn, C.D.: Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system. Computers and Education 48 (2007)
Sluijsmans, D.: The use of self-, peer- and co-assessment in higher education: a review of literature. Educational Technology Expertise Centre Open University of the Netherlands, Heerlen (1998)
Goldin, I.M., Ashley, K.D., Pinkus, R.L.: Teaching Case Analysis through Framing: Prospects for an ITS in an Ill-defined Domain. In: Workshop on Intelligent Tutoring Systems for Ill-Defined Domains, 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan (2006)
VanLehn, K., Lynch, C., Schulze, K., Shapiro, J., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The Andes Physics Tutoring System: Lessons Learned. International Journal of Artificial Intelligence and Education 15 (2005)
Koedinger, K., Anderson, J., Hadley, W., Mark, M.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8, 30–43 (1997)
Voss, J.: Toulmin’s Model and the Solving of Ill-Structured Problems. In: Arguing on the Toulmin Model: New Essays in Argument Analysis and Evaluation. Springer, Heidelberg (2006)
Russell, A.: Calibrated Peer Review: A writing and critical thinking instructional tool. In: Invention and Impact: Building Excellence in Undergraduate Science, Technology, Engineering and Mathematics (STEM) Education. American Association for the Advancement of Science (2004)
Gehringer, E.: Strategies and mechanisms for electronic peer review. In: 30th Annual Frontiers in Education Conference, vol. 1, pp. F1B/2-F1B/7 (2000)
Zhi-Feng Liu, E., Lin, S., Chiu, C., Yuan, S.: Web-based peer review: the learner as both adapter and reviewer. IEEE Transactions on Education 44, 246–251 (2001)
Masters, J., Madhyastha, T., Shakouri, A.: ExplaNet: A collaborative learning tool and hybrid recommender system for student-authored explanations. Journal of Interactive Learning Research 19, 51–74 (2008)
Hsiao, I., Brusilovsky, P.: Modeling peer review in example annotation. In: 16th International Conference on Computers in Education, Taipei, Taiwan, pp. 357–362 (2008)
Gouli, E., Gogoulou, A., Grigoriadou, M.: Supporting self-, peer-, and collaborative- assessment in e-learning: the case of the PEer and Collaborative ASSessment Environment (PECASSE). Journal of Interactive Learning Research 19, 615 (2008)
Nelson, M., Schunn, C.D.: The nature of feedback: how different types of peer feedback affect writing performance (2008)
Walvoord, M.E., Hoefnagels, M.H., Gaffin, D.D., Chumchal, M.M., Long, D.A.: An analysis of Calibrated Peer Review (CPR) in a science lecture classroom. Journal of College Science Teaching 37, 66–73 (2008)
Pinkus, R., Gloeckner, C., Fortunato, A.: Professional knowledge and applied ethics: a cognitive science approach (under review)
McNamara, D., Kintsch, E., Songer, N., Kintsch, W.: Are good texts always better? interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction 14, 1–43 (1996)
Wooley, R., Was, C.A., Schunn, C.D., Dalton, D.W.: The effects of feedback elaboration on the giver of feedback. In: Love, B.C., McRae, K., Sloutsky, V.M. (eds.) Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 2375–2380. Cognitive Science Society, Washington (2008)
Hübner, S., Nückles, M., Renkl, A.: Prompting cognitive and metacognitive processing in writing-to-learn enhances learning outcomes. In: 28th Annual Conference of the Cognitive Science Society (2006)
Nückles, M., Hübner, S., Renkl, A.: Enhancing self-regulated learning by writing learning protocols. Learning and Instruction 19, 259–271 (2009)
King, A.: ASK to THINK-TEL WHY: A model of transactive peer tutoring for scaffolding higher level complex learning. Educational Psychologist. 32, 221–235 (1997)
Cho, K., Cho, Y.H.: Learning from ill-structured cases. In: 29th Annual Cognitive Science Society Conference, p. 1722. Cognitive Science Society, Austin (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goldin, I.M., Ashley, K.D. (2010). Eliciting Informative Feedback in Peer Review: Importance of Problem-Specific Scaffolding. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13388-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-13388-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13387-9
Online ISBN: 978-3-642-13388-6
eBook Packages: Computer ScienceComputer Science (R0)