Einari Kurvinen

Erkki Kaila

Mikko Apiola

Information Technology and Nanotechnology

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Copyright © 2019 for this paper by its authors. Use permitted under Creative CommonsLicense Attribution 4.0 International (CC BY 4.0).Ethical Considerations in Learning Analytics: Ideas andDiscussionExtended abstractKaila Erkki 1, Kurvinen Einari2[0000-0002-7872-1743], Apiola Mikko 21University of Helsinki2University of Turkuerkki.kaila@helsinki.fi, emakur@utu.fi, mikko.apiola@utu.fiDesigning and developing the digital world crucially requires ethical perspectives.Common threats of today are algorithm-driven attempts to guide behavior, large-scaledata-intensive analysis of technology usage and movements, personally tailored fakenews, and new social issues generated by job-loss to automatisation. Learning analyt-ics, the analysis of data generated by digital learning systems, is not without its ownspecial set of ethical issues. In this short paper we wish to contribute and take part inthis discourse and lay out an agenda for future discussions and activities.Learning analytics is often considered as an essential part of the future of learningand teaching (Johnson et al. 2016). Learning analytics is often associated tightly withbig data, artificial intelligence and machine learning (Elias 2011, Ferguson et al. 2012,Siemens & Baker 2012). The subjects of analysis in learning analytics can be anyone;kids, adolescents, adults or the elderly. This poses a new set of sensitive concerns inprivacy and pedagogy for data analytics. Research scenarios in learning analytics ofteninvolve complex ethical considerations. An equal chance for learning should not bejeopardized. For a meaningful analysis, it is often necessary to combine data from learn-ing activities with various background factors. However, such data is often sensitiveand not easily available to analysis. Richer data means higher quality learning analytics.On the other hand, it means more ethical concerns, one of which is the pedagogicallywise use of the results. If learning analytics is able to detect. eg. learning deficits orspecial talents, how should this information be used in a pedagogically wise way?According to Slade & Prinsloo (2013), the ethical issues of learning analytics can bedivided into three categories:1. issues with location and interpretation of data,2. issues with consent, privacy and anonymization, and3. issues with data management.Many of the issues in learning analytics are typical to data analytics in general. Still,some generally accepted rules in privacy might prove to be problematic in educationalcontexts. The General Data Protection Regulation (GDPR 2019) of European Union62naturally affects educational technology as well. For example, the “right to be forgot-ten”, i.e. the right to have all data about you erased from a system, may be reasonablefor a social media platform that you are no longer using, but may be problematic if yourdata in an educational system is used as a basis for your evaluation and grade.Another example is automatic profiling, which according to Pönkä (2019) means“automatic classification that is based on an individual's properties, interests and prob-abilities” or “automatic evaluations, recommendations and interpretations”. Again, thismay sound reasonable to forbid if we are considering advertisement profilers. However,it might also mean that some scenarios in learning analytics become impossible. Suchscenarios might include, eg. automatically identifying students at risk of failing a courseor likely to repeat classes. In some systems, profiling is not done automatically, but thesystems provide analytics data to support the decision-making of a teacher.Slater (2015) has presented a taxonomy of ethical, legal and logistical issues in learn-ing analytics, with a number of questions to consider. For example, what potential neg-ative consequences for a student, such as isolation, bullying, could potentially resultfrom opting out of voluntary data collection such as school photography. On the otherhand, what is the impact of such incomplete or missing data for learning analytics?Other valid questions concern anonymity are: should students be able to discuss incourse forums anonymously, and should students be able to decide what kind of data iscollected and how it is used in analytics? And if so, what kind of outcomes this wouldhave for example to plagiarism detection systems, if a number of students decide thattheir papers and theses should not be included in the systems’ archives? Moreover, howcan teachers use continuous assessment instead of exams, if students can decide whichdata can be used by the teacher? Lot of similar questions have been raised (LAC 2019)by the Learning Analytics Committee appointed by the Finnish Ministry of Educationand Culture.The ethical issues in learning analytics are both similar and different to issues in dataanalytics in general. This means that some of the general rules and regulations may bedifficult to apply as-is in educational contexts. In Finland, educational institutes alreadyhave more liberties than other institutes. For example, the copyright law is different forthem. Educational institutes (or municipalities) can also decide, if they wish to use adigital tool in primary education. Such decisions may hinder students’ and their guard-ians’ freedom to opt-out. After all, municipalities are responsible and required by lawto organize primary education. As seen in these few examples, educational institutes dohave some mitigations to general rules. But how much do the rules bend?AI-driven data analytics is often associated with threats such as algorithm-drivenattempts to guide our behavior, personally tailored fake-news and massive data-inten-sive analysis of our technology usage and movement patterns. Since widespread utili-zation of learning analytics is yet to be conducted, it is still possible to do it responsiblyby following strict ethical guidelines as long as we have an agreement of what thoseguidelines are. However, different stakeholders have different goals in learning analyt-ics, and agreeing on the common guidelines has already proven to be a complex task.63References1. Elias, T. (2011). Learning analytics. Learning, 1-22.2. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. Interna-tional Journal of Technology Enhanced Learning, 4(5/6), 304-317.3. GDPR. (2016).  General Data Protection Regulation.  Retreived from  https://eur-lex.eu-ropa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679 (Aug 13, 2019).4. Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMChorizon report: 2016 higher education edition (pp. 1-50). The New Media Consortium.5. LAC (2019). Learning Analytics Consortium. Retrieved fromhttps://wiki.eduuni.fi/pages/viewpage.action?pageId=61642250 (Aug. 13th, 2019).6. Pönkä (2019). Tietosuoja opetuksessa. Retrieved at Aug. 13th, 2019. https://www.sli-deshare.net/hponka/tietosuoja-ja-tietoturva-opetuksessa7. Siemens, G., & d Baker, R. S. (2012). Learning analytics and educational data mining: to-wards communication and collaboration. In Proceedings of the 2nd international conferenceon learning analytics and knowledge (pp. 252-254). ACM.8. Sclater, N. (2015). A taxonomy of ethical, legal and logistical issues of learning analyticsv1. 0.9. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. AmericanBehavioral Scientist, 57(10), 1510-1529.

Ethical Considerations in Learning Analytics: Ideas and Discussion

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