Abstract
With the rise of digital systems in learning scenarios in recent years as learning management systems, massive open online courses, serious games, and the use of sensors and IoT devices huge amounts of personal data are generated. In the context of learning analytics, this data is used to individualize contents and exercises, predict success or dropout. Based on a meta analysis it is investigated to which extent the privacy of learners is respected. Our research found that, although surveys have shown that privacy is a concern for learners and critical to adopt to establish trust in learning analytic solutions, privacy issues are very rarely addressed in actual learning analytic setups.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
What is learning analytics. https://www.solaresearch.org/about/what-is-learning-analytics/
Albrecht, M., et al.: Homomorphic encryption security standard. Technical report, HomomorphicEncryption.org, Toronto, Canada, November 2018
Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, LAK 2012, Vancouver, British Columbia, Canada, pp. 267–270. Association for Computing Machinery, April 2012. https://doi.org/10.1145/2330601.2330666
Bernstein, D.J.: Chacha, a variant of salsa20. In: Workshop Record of SASC, vol. 8, pp. 3–5 (2008)
Bernstein, D.J., Duif, N., Lange, T., Schwabe, P., Yang, B.Y.: High-speed high-security signatures. J. Cryptogr. Eng. 2(2), 77–89 (2012). https://doi.org/10.1007/s13389-012-0027-1
Bosch, N., Crues, R.W., Paquette, L., Shaik, N.: “Hello, [REDACTED]”: protecting student privacy in analyses of online discussion forums. EDM (2020)
Chen, G., Rolim, V., Mello, R.F., Gašević, D.: Let’s shine together! a comparative study between learning analytics and educational data mining. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, LAK 2020, Frankfurt, Germany, pp. 544–553. Association for Computing Machinery, March 2020. https://doi.org/10.1145/3375462.3375500
Corrin, L., et al.: The ethics of learning analytics in Australian higher education (2019). https://melbourne-cshe.unimelb.edu.au/research/research-projects/edutech/the-ethical-use-of-learning-analytics
Daemen, J., Rijmen, V.: Reijndael: the advanced encryption standard. Dr. Dobb’s J. Softw. Tools Prof. Program. 26(3), 137–139 (2001)
Drachsler, H., Greller, W.: Privacy and analytics: it’s a DELICATE issue a checklist for trusted learning analytics. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, LAK 2016, Edinburgh, United Kingdom, pp. 89–98. Association for Computing Machinery, April 2016. https://doi.org/10.1145/2883851.2883893
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Flanagan, B., Ogata, H.: Integration of learning analytics research and production systems while protecting privacy. In: The 25th International Conference on Computers in Education, Christchurch, New Zealand, pp. 333–338 (2017)
Hansen, M., Jensen, M., Rost, M.: Protection goals for privacy engineering. In: 2015 IEEE Security and Privacy Workshops, pp. 159–166. IEEE (2015)
Hermann, O., Hansen, J., Rensing, C., Drachsler, H.: Verhaltenskodex für trusted learning analytics, March 2020. https://doi.org/10.13140/RG.2.2.24859.41760
Hernández-Lara, A.B., Perera-Lluna, A., Serradell-López, E.: Applying learning analytics to students’ interaction in business simulation games. the usefulness of learning analytics to know what students really learn. Comput. Hum. Behav. 92, 600–612 (2019)
Karumbaiah, S., Baker, R.S.J.D., Shute, V.J.: Predicting quitting in students playing a learning game. In: EDM (2018)
Kim, B.H., Vizitei, E., Ganapathi, V.: GritNet: student performance prediction with deep learning. In: EDM (2018)
Klose, M., Desai, V., Song, Y., Gehringer, E.: EDM and privacy: ethics and legalities of data collection, usage, and storage. In: EDM (2020)
Käser, T., Schwartz, D.L.: Exploring neural network models for the classification of students in highly interactive environments. In: EDM 2019, International Educational Data Mining Society, July 2019. https://eric.ed.gov/?id=ED599211
Murmann, P., Reinhardt, D., Fischer-Hübner, S.: To be, or not to be notified. In: Dhillon, G., Karlsson, F., Hedström, K., Zúquete, A. (eds.) SEC 2019. IAICT, vol. 562, pp. 209–222. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22312-0_15
Mwalumbwe, I., Mtebe, J.S.: Using learning analytics to predict students’ performance in Moodle learning management system: a case of Mbeya University of Science and Technology. Electron. J. Inf. Syst. Dev. Countries 79(1), 1–13 (2017). https://doi.org/10.1002/j.1681-4835.2017.tb00577.x
Papamitsiou, Z., Giannakos, M.N., Ochoa, X.: From childhood to maturity: are we there yet? Mapping the intellectual progress in learning analytics during the past decade. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, LAK 2020, Frankfurt, Germany, pp. 559–568. Association for Computing Machinery, March 2020. https://doi.org/10.1145/3375462.3375519
Pardo, A., Siemens, G.: Ethical and privacy principles for learning analytics. Br. J. Educ. Technol. 45(3), 438–450 (2014). https://doi.org/10.1111/bjet.12152
Pelaez, K., Levine, R., Fan, J., Guarcello, M., Laumakis, M.: Using a latent class forest to identify at-risk students in higher education. In: EDM 2019 (2019). https://doi.org/10.5281/zenodo.3554747
Pirkl, G., et al.: Any problems? a wearable sensor-based platform for representational learning-analytics. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 353–356 (2016)
Sclater, N.: Developing a code of practice for learning analytics. J. Learn. Anal. 3(1), 16–42 (2016). https://doi.org/10.18608/jla.2016.31.3
Tsai, Y.S., Whitelock-Wainwright, A., Gašević, D.: The privacy paradox and its implications for learning analytics. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, LAK 2020, Frankfurt, Germany, pp. 230–239. Association for Computing Machinery, March 2020. https://doi.org/10.1145/3375462.3375536
Wampfler, R., Klingler, S., Solenthaler, B., Schinazi, V., Gross, M.: Affective state prediction in a mobile setting using wearable biometric sensors and stylus (2019). https://doi.org/10.3929/ethz-b-000393912
Whitelock-Wainwright, A., et al.: Assessing the validity of a learning analytics expectation instrument: a multinational study. J. Comput. Assist. Learn. (2020). https://doi.org/10.1111/jcal.12401
Acknowledgement
This work was supported by the Leibniz Association and the Ministry for Science and Culture of Lower Saxony as part of Leibniz ScienceCampus – Postdigital Participation – Braunschweig.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Priedigkeit, M., Weich, A., Schiering, I. (2021). Learning Analytics and Privacy—Respecting Privacy in Digital Learning Scenarios. In: Friedewald, M., Schiffner, S., Krenn, S. (eds) Privacy and Identity Management. Privacy and Identity 2020. IFIP Advances in Information and Communication Technology, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-030-72465-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-72465-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72464-1
Online ISBN: 978-3-030-72465-8
eBook Packages: Computer ScienceComputer Science (R0)