Abstract
Peer-reviewing is a recommended instructional technique to encourage good writing. Peer reviewers, however, may fail to identify key elements of an essay, such as thesis and conclusion statements, especially in high school writing. Our system identifies thesis and conclusion statements, or their absence, in students’ essays in order to scaffold reviewer reflection. We showed that computational linguistics and interactive machine learning have the potential to facilitate peer-review processes.
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References
National Center for Education Statistics, The Nation’s Report Card: Writing, Institute of Education Sciences, US Department of Education, Washington, D.C. (2012)
Sadler, P., Good, E.: The impact of self-and peer-grading on student learning. Educational Assessment 11(1), 1–31 (2006)
Wooley, R., Was, C., Schunn, C., Dalton, D.: The effects of feedback elaboration on the giver of feedback. Paper presented at the 30th Annual Meeting of the Cognitive Science Society (2008)
Cho, K., Schunn, C.: Developing writing skills through students giving instructional explanations. In: Stein, Kucan (eds.) Instructional Explanations in the Disciplines. Springer, NY (2010)
Goldin, I.M., Ashley, K., Schunn, C.D.: Redesigning Educational Peer Review Interactions Using Computer Tools: An Introduction. Journal of Writing Research 4(2), 111–119 (2012)
Hansen, J., Liu, J.: Guiding principles for effective peer response. ELT J. 59(1), 31–38 (2005)
Durst, R.: Cognitive and Linguistic Demands of Analytic Writing. Research in the Teaching of English 21(4), 347–376 (1987)
National Assessment of Educational Progress, Writing Framework for the, National Assessment of Educational Progress (2011)
Shermis, M.D., Burstein, J., Higgins, D., Zechner, K.: Automated essay scoring: Writing assessment and instruction. International Encyclopedia of Education 4, 20–26 (2010)
Burstein, J., Marcu, D.: A machine learning approach for identification thesis and conclusion statements in student essays. Computers and the Humanities 37(4), 455–467 (2003)
Burstein, J., Marcu, D., Knight, K.: Finding the WRITE stuff: Automatic identification of discourse structure in student essays. IEEE Intelligent Systems 18(1), 32–39 (2003)
Roscoe, R.D., McNamara, D.S.: Writing pal: Feasibility of an intelligent writing strategy tutor in the high school classroom. Journal of Educational Psychology 105(4), 1010 (2013)
Crossley, S.A., McNamara, D.S.: Understanding expert ratings of essay quality: Coh-Metrix analyses of first and second language writing. International Journal of Continuing Engineering Education and Life Long Learning 21(2), 170–191 (2011)
Cho, K., Schunn, C.D.: Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system. Computers & Education 48(3), 409–426 (2007)
Fails, J.A., Olsen Jr, D.R.: Interactive machine learning. In: Proceedings 8th International Conference on Intelligent User Interfaces, pp. 39–45 (2003)
De La Paz, S., Graham, S.: Explicit teaching strategies, skills and knowledge: Writing instruction in middle school classrooms. Journal of Educational Psychology 94(4), 687–698 (2002)
Durst, R., Laine, C., Schultz, L.M., Vilter, W.: Appealing Texts The Persuasive Writing of High School Students. Written Communication 7(2), 232–255 (1990)
Fleiss, J.L., Cohen, J., Everitt, B.S.: Large sample standard errors of kappa and weighted kappa. Psychological Bulletin 72(5), 323 (1969)
Marcu, D.: Discourse trees are good indicators of importance in text. Advances in Automatic Text Summarization, 123–136 (1999)
Kakwani, N.: On a class of poverty measures. Econometrica: Journal of the Econometric Society, 437–446 (1980)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935–940 (2006)
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Falakmasir, M.H., Ashley, K.D., Schunn, C.D., Litman, D.J. (2014). Identifying Thesis and Conclusion Statements in Student Essays to Scaffold Peer Review. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_31
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DOI: https://doi.org/10.1007/978-3-319-07221-0_31
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