Abstract
In massive open online courses (MOOCs), peer grading will play an important role to promote MOOCs development. In this paper, we develop a peer grading tool for programming courses on MOOCs. It is capable of dealing with large and diverse student population, and providing them with targeted subjective assessment. This tool firstly partition the submissions into small chunks to reduce the task of reviewers and give us flexibility to scale the code review process. Next we use code normalization and chunks clustering to assign similar chunks to the same student for increasing reviewer efficiency. Besides, the tool use a random allocation strategy and workload classification to assure reviewers workload balance while every student can get diverse feedback. Finally our evaluation experiments on a number of students in school indicate that the tool has achieved a significant improvement over the peer grading on MOOCs.
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References
Breslow, L., Pritchard, D.E., DeBoer, J., Stump, G.S., Ho, A.D., Seaton, D.: Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research & Practice in Assessment 8, 13–25 (2013)
Pappano, L.: The Year of the MOOC. The New York Times 2 (2012)
Cheang, B., Kurnia, A., Lim, A., Oon, W.-C.: On automated grading of programming assignments in an academic institution. Computers & Education 41, 121–131 (2003)
Ihantola, P., Ahoniemi, T., Karavirta, V., Seppälä, O.: Review of recent systems for automatic assessment of programming assignments. In: Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pp. 86–93 (2010)
Ertmer, P.A., Richardson, J.C., Belland, B., Camin, D., Connolly, P., Coulthard, G., et al.: Using peer feedback to enhance the quality of student online postings: An exploratory study. Journal of Computer Mediated Communication 12, 412–433 (2007)
Kulkarni, C., Wei, K.P., Le, H., Chia, D., Papadopoulos, K., Cheng, J., et al.: Peer and self assessment in massive online classes. ACM Transactions on Computer-Human Interaction (TOCHI)Â 20, 33 (2013)
Shah, N.B., Bradley, J.K., Parekh, A., Wainwright, M., Ramchandran, K.: A Case for Ordinal Peer-evaluation in MOOCs. Presented at the NIPS Workshop on Data Driven Education, Harrah’s Tahoe D (2013)
Liu, E.Z., Lin, S.S., Yuan, S.: Alternatives to Instructor Assessment: A Case Study of Comparing Self and Peer Assessment with Instructor Assessment under a Networked Innovative Assessment Procedures. International Journal of Instructional Media 29, 395–404 (2002)
Weld, D.S., Adar, E., Chilton, L., Hoffmann, R., Horvitz, E., Koch, M., et al.: Personalized online education—a crowdsourcing challenge. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)
Piech, C., Huang, J., Chen, Z., Do, C., Ng, A., Koller, D.: Tuned models of peer assessment in MOOCs, arXiv preprint arXiv:1307.2579 (2013)
Sadler, P.M., Good, E.: The impact of self-and peer-grading on student learning. Educational Assessment 11, 1–31 (2006)
Tang, M., Miller, R.C., Smith, A.C.: Caesar: A Social Code Review Tool for Programming Education (August 22, 2011)
Schleimer, S., Wilkerson, D.S., Aiken, A.: Winnowing: local algorithms for document fingerprinting. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 76–85 (2003)
Sorokina, D., Gehrke, J., Warner, S., Ginsparg, P.: Plagiarism detection in arXiv. In: Sixth International Conference on Data Mining, ICDM 2006, pp. 1070–1075 (2006)
Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32, 241–254 (1967)
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Wei, Z., Wu, W. (2015). A Peer Grading Tool for MOOCs on Programming. In: Wang, H., et al. Intelligent Computation in Big Data Era. ICYCSEE 2015. Communications in Computer and Information Science, vol 503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46248-5_46
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DOI: https://doi.org/10.1007/978-3-662-46248-5_46
Publisher Name: Springer, Berlin, Heidelberg
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