%0 Book Section %1 Joksimović2022 %A Joksimović, Srećko %A Marshall, Ruth %A Rakotoarivelo, Thierry %A Ladjal, Djazia %A Zhan, Chen %A Pardo, Abelardo %B Manage Your Own Learning Analytics: Implement a Rasch Modelling Approach %C Cham %D 2022 %E McKay, Elspeth %I Springer International Publishing %K data datascience education ethics infrastructure learninganalytics measurement privacy riskmanagement %P 1--22 %R 10.1007/978-3-030-86316-6_1 %T Privacy-Driven Learning Analytics %U https://doi.org/10.1007/978-3-030-86316-6_1 %X Data privacy is a central theme in the global dialogue around the application of data science in education. Despite the growing need, research organisations and private companies working on education and learning analytics solutions still rely on ad hoc, red-tape-heavy and inconsistent approaches to privacy protection. This chapter outlines the substantial and growing body of work on data privacy risk measurement and reduction which can help address this problem and enable better use of online learning data with improved privacy risk management. The combination of privacy risk measurement and reduction tools with a sound privacy risk management framework has the potential to manage privacy risk reliably and consistently across all datasets in a pragmatic and cost-effective way as tools evolve to integrate with standard data management infrastructure. %@ 978-3-030-86316-6

@inbook{Joksimović2022, abstract = {Data privacy is a central theme in the global dialogue around the application of data science in education. Despite the growing need, research organisations and private companies working on education and learning analytics solutions still rely on ad hoc, red-tape-heavy and inconsistent approaches to privacy protection. This chapter outlines the substantial and growing body of work on data privacy risk measurement and reduction which can help address this problem and enable better use of online learning data with improved privacy risk management. The combination of privacy risk measurement and reduction tools with a sound privacy risk management framework has the potential to manage privacy risk reliably and consistently across all datasets in a pragmatic and cost-effective way as tools evolve to integrate with standard data management infrastructure.}, added-at = {2021-12-12T12:03:00.000+0100}, address = {Cham}, author = {Joksimovi{\'{c}}, Sre{\'{c}}ko and Marshall, Ruth and Rakotoarivelo, Thierry and Ladjal, Djazia and Zhan, Chen and Pardo, Abelardo}, biburl = {https://www.bibsonomy.org/bibtex/2cb72072de77c8dc55db1d3d53edd14cd/ereidt}, booktitle = {Manage Your Own Learning Analytics: Implement a Rasch Modelling Approach}, doi = {10.1007/978-3-030-86316-6_1}, editor = {McKay, Elspeth}, interhash = {a4de78341d9cd252e6ca1e7571261fc4}, intrahash = {cb72072de77c8dc55db1d3d53edd14cd}, isbn = {978-3-030-86316-6}, keywords = {data datascience education ethics infrastructure learninganalytics measurement privacy riskmanagement}, pages = {1--22}, publisher = {Springer International Publishing}, timestamp = {2021-12-12T12:03:00.000+0100}, title = {Privacy-Driven Learning Analytics}, url = {https://doi.org/10.1007/978-3-030-86316-6_1}, year = 2022 }