Abstract
Peer-assessment entails, for students, a very beneficial learning activity, from a pedagogical point of view. The peer-evaluation can be performed over a variety of peer-produced resources, the principle being that the more articulated such resource is, the better. Here we focus, in particular, on the automated support to grading open answers, via a peer-evaluation-based approach, which is mediated by the (partial) grading work of the teacher, and produces a (partial, as well) automated grading. We propose to support such automated grading by means of a method based on the K-NN technique. This method is an alternative to a previously studied and implemented one, based on Bayesian Networks. Here we describe the new approach and provide the reader with a preliminary evaluation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of educational objectives: the classification of educational goals. In: Handbook I: Cognitive Domain. McGraw-Hill Inc., New York (1956)
Castellanos-Nieves, D., Fernandez-Breis, J., Valencia-Garcia, R., Martinez-Bejar, R., Iniesta-Moreno, M.: Semantic web technologies for supporting learning assessment. Inf. Sci. 181(9), 1517–1537 (2011)
Conati, C., Gartner, A., Vanlehn, K.: Using Bayesian networks to manage uncertainty in student modeling. User Model. User-Adap. Inter. 12, 371–417 (2002)
De Marsico, M., Sciarrone, F., Sterbini, A., Temperini, M.: The impact of self- and peer grading on student learning. EURASIA J. Math. Sci. Technol. Educ. 13(4), 1085–1106 (2017)
El-Kechaï, N., Delozanne, É., Prévit, D., Grugeon, B., Chenevotot, F.: Evaluating the performance of a diagnosis system in school algebra. In: Leung, H., Popescu, E., Cao, Y., Lau, Rynson W.H., Nejdl, W. (eds.) ICWL 2011. LNCS, vol. 7048, pp. 263–272. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25813-8_28
Hoshino, A., Nakagawa, H.: A real-time multiple-choice question generation for language testing - a preliminary study. In: Proceedings of 2nd Workshop on Building Educational Applications Using NLP, pp. 17–20 (2005)
Li, B., Lu, J., Yao, J.-M., Zhu, Q.-M.: Automated essay scoring using the KNN algorithm. In: Proceedings of International Conference on Computer Science and Software Engineering (2008)
Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F.: A teacher model to speed up the process of building courses. In: Kurosu, M. (ed.) HCI 2013. LNCS, vol. 8005, pp. 434–443. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39262-7_50
Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F., Temperini, M.: A recommendation module to help teachers build courses through the moodle learning management system. New Rev. Hypermed. Multimed. 2(1–2), 58–82 (2015)
Metcalfe, J., Shimamura, A.P.: Metacognition: Knowing About Knowing. MIT Press, Cambridge (1994)
Mitchell, T.M.: Machine Learning, 1st edn. David McKay, New York (1997)
Palmer, K., Richardson, P.: On-line assessment and free-response input-a pedagogic and technical model for squaring the circle. In: Proceedings of 7th CAA Conference, pp. 289–300 (2003)
Sadler, P.M., Good, E.: The impact of self- and peer-grading on student learning. Educ. Assess. 11(1), 1–31 (2006)
Yamanishi, K., Li, H.: Mining open answers in questionnaire data. IEEE Intell. Syst. 17, 58–63 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
De Marsico, M., Sterbini, A., Sciarrone, F., Temperini, M. (2017). Modeling a Peer Assessment Framework by Means of a Lazy Learning Approach. In: Huang, TC., Lau, R., Huang, YM., Spaniol, M., Yuen, CH. (eds) Emerging Technologies for Education. SETE 2017. Lecture Notes in Computer Science(), vol 10676. Springer, Cham. https://doi.org/10.1007/978-3-319-71084-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-71084-6_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71083-9
Online ISBN: 978-3-319-71084-6
eBook Packages: Computer ScienceComputer Science (R0)