Abstract
In recent years, online learning has received widespread attention, and has played an important role in recent years. As a new application field of crowdsourcing system, peer assessment can solve student’s performance evaluation problem in massive online courses. Traditional crowdsourcing quality control algorithm has not been able to use effective information to the evaluation of workers. Aiming at this problem, a quality control algorithm based on multi-dimensional information is proposed. The user’s behavior, comment text information and other useful elements are combined together. The feature vectors of reliability are extracted from a variety of information based on the frame of log-linear model. And then, the gradient descent algorithm model is used to study the optimal parameters. The experimental results show that when comparing with the traditional Expectation Maximization algorithm, our multi-dimensional quality control algorithm has better performance in the accuracy and mean square error.
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
Kulkarni, C., et al.: Peer and self-assessment in massive online classes. Des. Think. Res. 20(6), 1–31 (2013)
Zhang, Z., et al.: Crowdsourcing quality control strategy and evaluation algorithm. Chin. J. Comput. 8, 1636–1649 (2013)
Shah, N.B., Bradley, J.K., et al.: A case for ordinal peer-evaluation in MOOCs. In: NIPS Workshop on Data Driven Education, pp. 1–8 (2013)
Daniel, J.: Making sense of MOOCs: musings in a maze of myth, paradox and possibility. J. Interact. Media Educ. 2012(3), 3–12 (2012)
Zelinski, M., Hicks, N.M., et al.: Instructor outcomes of teaching a STEM MOOC. In: IEEE Frontiers in Education Conference 2017, Indianapolis, IN, USA, vol. 1, pp. 1–7 (2017)
Dong, K., et al.: The evolution path and future prospect of research on crowdsourcing in China and abroad. Sci. Technol. Prog. Policy 8, 154–160 (2016)
Zheng, Z., et al.: Crowdsourcing quality evaluation algorithm based on sliding task window. Mini-micro Syst. 9, 2125–2129 (2017)
Felsenthal, D.S., et al.: The treaty of nice and qualified majority voting. Soc. Choice Welf. 18(3), 431–464 (2001)
Moon, T.K.: The expectation-maximization algorithm. IEEE Sig. Process. Mag. 13(6), 47–60 (2000)
Zhang, Z., et al.: Research on quality control of peer grading in MOOCs. Softw. Eng. 2, 7–10 (2018)
Sun, J., Yang, X., Wang, B.: Crowdsourced indoor localization for diverse devices with RSSI sequences. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 614–625. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30952-7_62
Ding, Y., et al.: Research on crowdsourcing quality control algorithm based on social platform. Softw. Guide 12, 90–93 (2017)
Wang, B., et al.: Application of log-linear model in part-of-speech tagging. Comput. Sci. 35(5), 163–166 (2008)
Christensen, R.: Log-linear models and logistic regression, pp. 267–268. Springer, New York (2006)
Bengio, Y., et al.: A neural probabilistic language model. J. Mach. Learn. Res. 3(6), 1137–1155 (2003)
Kesorn, K., et al.: An enhanced bag-of-visual word vector space model to represent visual content in athletics images. IEEE Trans. Multimed. 14(1), 211–222 (2012)
Acknowledgements
This work is supported by the Fundamental Research Funds for the Central Universities under Grant N181604015, and the National Natural Science Foundation of China under Grant No. 61602106.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, P., Yin, Z., Li, F. (2020). Quality Control Method for Peer Assessment System Based on Multi-dimensional Information. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-60029-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60028-0
Online ISBN: 978-3-030-60029-7
eBook Packages: Computer ScienceComputer Science (R0)
