Abstract
Today’s e-learning systems enable students to communicate with peers (or co-learners) to ask or provide feedback, leading to more efficient learning. Unfortunately, this new option comes with significantly increased risks to the privacy of the feedback requester as well as the peers involved in the feedback process. In fact, peers may unintentionally disclose personal information which may cause great threats to them like cyber-bullying, which in turn may create an unfavorable learning environment leading individuals to abandon learning. In this paper, we propose an approach to minimize data self-disclosure and privacy risks in e-learning contexts. It consists first of mining peers’ feedback to remove negative comments (reducing bullying and harassment) based on machine learning classifier and natural language processing techniques. Second, it consists of striping sentences that potentially reveal personal information in order to protect learners from self-disclosure risks, based on Latent Semantic Analysis (LSA).
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Selmi, M., Hage, H., Aïmeur, E. (2015). Latent Semantic Analysis for Privacy Preserving Peer Feedback. In: Lopez, J., Ray, I., Crispo, B. (eds) Risks and Security of Internet and Systems. CRiSIS 2014. Lecture Notes in Computer Science(), vol 8924. Springer, Cham. https://doi.org/10.1007/978-3-319-17127-2_7
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DOI: https://doi.org/10.1007/978-3-319-17127-2_7
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