Abstract
The first draft of an undergraduate student thesis generally presents deficiencies, which must be polished with the help of the academic advisor to get an acceptable document. However, this task is repeated every time a student prepares his thesis, becoming extra time spent by the advisor. Our work seeks to help the student improve the writing, based on intelligent tutoring and natural language processing techniques. For the current study, we focus primarily on the conclusions section of a thesis. In this paper we present three tutoring system components: Identifying Weak Sentences, Classifying the Weak Sentences, Customizing Feedback to Students. Our system identifies weaknesses in sentences, such as the use of general instead of specific terms, or the absence of reflections and personal opinions. We provide initial models and their evaluations for each component.
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References
Muñoz, C.: Como elaborar y asesorar una Investigación de tesis. Prentice Hall (2011)
Crossley, S.A., Varner, L.K., Roscoe, R.D., McNamara, D.S.: Using Automated Indices of Cohesion to Evaluate an Intelligent Tutoring System and an Automated Writing Evaluation System. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 269–278. Springer, Heidelberg (2013)
Crossley, S., Roscoe, R., McNamara, D.: Using Automatic Scoring Models to Detect Changes in Student Writing in an Intelligent Tutoring System. In: Procs. 26th FLAIRS, pp. 208–213 (2013)
Olney, A.M., D’Mello, S., Person, N., Cade, W., Hays, P., Williams, C., Lehman, B., Graesser, A.: Guru: A Computer Tutor That Models Expert Human Tutors. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 256–261. Springer, Heidelberg (2012)
Graesser, A., D’Mello, S., Craig, S., Witherspoon, A., Sullins, B., McDaniel, B., Gholson, B.: The Relationship between Affective States and Dialog Patterns during Interactions with Autotutor. Interactive Learning Research 19, 293–312 (2008)
Bethard, S., Okoye, I., Arafat, S., Hang, M.J., Sumner, T.: Identifying science concepts and student misconceptions in an interactive essay writing tutor. In: Procs. of the 7th Workshop on Building Educational Applications Using NLP, pp. 12–21 (2012)
Bellegarda, J.: Unsupervised document clustering using multiresolution latent semantic density analysis. In: Workshop on Machine Learning for Signal Processing, pp. 361–366 (2010)
García Gorrostieta, J.M., González López, S., López-López, A., Carrillo, M.: An intelligent tutoring system to evaluate and advise on lexical richness in students writings. In: Hernández-Leo, D., Ley, T., Klamma, R., Harrer, A. (eds.) EC-TEL 2013. LNCS, vol. 8095, pp. 548–551. Springer, Heidelberg (2013)
Kilicoglu, H., Bergler, S.: Recognizing speculative language in biomedical research articles: a linguistically motivated perspective. In: Procs. of the Workshop on Current Trends in Biomedical Natural Language Processing, Stroudsburg, PA, USA, pp. 46–53 (2010)
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López, S.G., Bethard, S., López-López, A. (2014). Identifying Weak Sentences in Student Drafts: A Tutoring System. In: Mascio, T., Gennari, R., Vitorini, P., Vicari, R., de la Prieta, F. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning. Advances in Intelligent Systems and Computing, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-319-07698-0_10
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DOI: https://doi.org/10.1007/978-3-319-07698-0_10
Publisher Name: Springer, Cham
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