Abstract
Learning analytics is used to measure, collect and analyse educational data. It extracts valuable information from educational data of students for the purpose of understanding and optimizing educational practices. It helps in developing predictive models using past educational data of students to improve future practices. It benefits stakeholders to improve learning processes by providing timely feedback regarding their progress. Although sharing and using of data include privacy risks for data subjects, privacy is the right of individuals to secure his/her personal information. Data privacy is a concern of sharing the data with a third party. The k-anonymization is the most popular existing privacy technique which helps to resolve privacy issues, but it also has some drawbacks. It leads to loss of information that is dependent on the value of k parameter. This paper discusses in detail about learning analytic, need for privacy in learning analytics, existing privacy preservation techniques and various existing privacy issues in learning analytics. An exhaustive literature survey has been done, and comparison between various existing privacy techniques has been presented.
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References
M.E. Gursoy, A. Inan, M.E. Nergiz, Y. Saygin, Privacy-preserving learning analytics: challenges and techniques. IEEE Trans. Learn. Technol. 10(1) (2017).
V. Mayer-Schonberger, K. Cukier, in Learning with Big Data: The Future of Education. (Boston, MA, USA, Houghton Mifflin)
A. Pardo, G. Siemens, Ethical and privacy principles for learning analytics. Br. J. Educ. Technol. 45(3), 438–450 (2014). https://doi.org/10.1111/bjet.12152
S.P. Gangadharan, Digital inclusion and data profiling. First Monday 17(5), (2012)
D. Boyd, K. Crawford, in Six Provocations for Big Data (2011)
N. Singer, in InBloom Student Data Repository to Close, (New York Times, 2014).
D.J. Solove, A taxonomy of privacy. Univ. Pennsylvania Law Rev. 154, 477–564 (2006)
D.J. Solove, Introduction: privacy self-management and the consent dilemma. Harvard Law Rev. 126, 1880–1903 (2012)
H. Drachsler, W. Greller, Privacy and analytics: it’s a DELICATE issue—a checklist for trusted learning analytics. in Proceedings of 6th Interantional Conference Learning Analytics Knowledge, (2016) pp. 89–98
J. Swenson, Establishing an ethical literacy for learning analytics. in Proceedings 4th Interantional Conference Learning Analytics Knowledge, March (2004) pp. 246–250
S. Venkatasubramania, t-Closeness: privacy beyond k-Anonymity and l-Diversity. in IEEE 23rd International Conference on Data Engineering (2007)
C. Dwork, Differential privacy. in Automata, Languages and Programming. (Berlin, Germany, Springer, 2006) pp. 1–12
S.C. di Vimercati, in Protecting Privacy in Data Release. Lecture Notes in Computer Science (2011)
B.K. Tripathy, Chapter 14 database anonymization techniques with focus on uncertainty and multisensitive attributes. (IGI Global, 2013)
P. Ram Mohan Rao, S. Murali Krishna, A.P. Siva Kumar, Privacy preservation techniques in big data analytics: a survey. J. Big Data (2018)
N. Li,, T. Li, S. Venkatasubramanian, Closeness: a new privacy measure for data publishing. IEEE Trans. Knowled. Data Eng. (2010)
M.H. Afifi Ibrahim, K. Zhou, J. Ren, Privacy characterization and quantification in data publishing. IEEE Trans. Knowled. Data Eng. (2018)
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Nisha, Singhal, A., Muttoo, S.K. (2021). Learning Analytics: A Literature Review and its Challenges. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-16-0882-7_53
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DOI: https://doi.org/10.1007/978-981-16-0882-7_53
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