Paul Prinsloo
ABSTRACT
As higher education increasingly moves to online and digital learning spaces, we have access not only to greater volumes of student data, but also to increasingly fine-grained and nuanced data. A significant body of research and existing practice are used to convince key stakeholders within higher education of the potential of the collection, analysis and use of student data to positively impact on student experiences in these environments. Much of the recent focus in learning analytics is around predictive modeling and uses of artificial intelligence to both identify learners at risk, and to personalize interventions to increase the chance of success.
In this paper we explore the moral and legal basis for the obligation to act on our analyses of student data. The obligation to act entails not only the protection of student privacy and the ethical collection, analysis and use of student data, but also, the effective allocation of resources to ensure appropriate and effective interventions to increase effective teaching and learning.
The obligation to act is, however tempered by a number of factors, including inter and intra-departmental operational fragmentation and the constraints imposed by changing funding regimes. Increasingly higher education institutions allocate resources in areas that promise the greatest return. Choosing (not) to respond to the needs of specific student populations then raises questions regarding the scope and nature of the moral and legal obligation to act. There is also evidence that students who are at risk of failing often do not respond to institutional interventions to assist them.
In this paper we build and expand on recent research by, for example, the LACE and EP4LA workshops to conceptually map the obligation to act which flows from both higher education's mandate to ensure effective and appropriate teaching and learning and its fiduciary duty to provide an ethical and enabling environment for students to achieve success. We examine how the collection and analysis of student data links to both the availability of resources and the will to act and also to the obligation to act. Further, we examine how that obligation unfolds in two open distance education providers from the perspective of a key set of stakeholders - those in immediate contact with students and their learning journeys - the tutors or adjunct faculty.
- Alexander, L. (2005). Lesser evils: A closer look at the paradigmatic justification. Law and Philosophy, 24(6), 611--643.Google Scholar
Cross Ref
- Altbach, P.G., Reisberg, L., & Rumbley, L.E. (2009). Trends in global higher education: Tracking an academic revolution. Retrieved from http://unesdoc.unesco.org/images/0018/001831/183168e.pdfGoogle Scholar
- Baijnath, N. (2015) Going digital, presented at the Learning Africa Conference, Ethiopia, 21 May 2015. Retrieved from: http://www.unisa.ac.za/news/wp-content/uploads/2015/05/eLearning-Africa-2015-Speaking-Notes-N-Baijnath-200515.pdfGoogle Scholar
- Blythe, S., Darabi, R., Kirkwood, B. S., & Baden, W. (2009). Exploring options for students at the boundaries of the" at-risk" designation. WPA: Writing Program Administration-Journal of the Council of Writing Program Administrators, 33, 9--28.Google Scholar
- Bos, W. & Tarnai, C. (1999). Content analysis in empirical social research, International Journal of Educational Research, 31, pp. 659--671.Google ScholarCross Ref
- Botes, A. (2000). A comparison between the ethics of justice and the ethics of care. Journal of Advanced Nursing, 32, 1071--1075.Google ScholarCross Ref
- Braxton, J.M. (Ed.). (2000). Reworking the student departure puzzle. Nashville: Vanderbilt University Press.Google Scholar
- Calvert, C. E. (2014). Developing a model and applications for probabilities of student success: a case study of predictive analytics. Open Learning: The Journal of Open, Distance and e-Learning, 29(2), 160--173.Google ScholarCross Ref
- Crawford, K., & Whittaker, M. (2016, September 12). Artificial intelligence is hard to see. Why we urgently need to measure AI's societal impacts. {Web log post}. Medium. Retrieved from https://medium.com/@katecrawford/artificial-intelligence-is-hard-to-see-a71e74f386db#.wi7sq513aGoogle Scholar
- Creswell, J.W. (2007). Qualitative inquiry & research design. Choosing among five approaches, 2nd edition, London, UK: SAGE Publications.Google Scholar
- Danaher, J. (2015, June 15). How might algorithms rule our lives? Mapping the logical space of algocracy. {Web log post}. Retrieved from http://philosophicaldisquisitions.blogspot.co.za/2015/06/how-might-algorithms-rule-our-lives.htmlGoogle Scholar
- Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982--1002. Google ScholarDigital Library
- Drachsler, H., & Greller, W. (2016). Privacy and learning analytics - it's a DELICATE issue. 6th Learning Analytics and Knowledge Conference 2016, April 25--29, 2016, pp. 89--98. Edinburgh, UK. Google ScholarDigital Library
- El-Khawas, E. (2000). Patterns of communication and miscommunication between research and policy. In S. Schwarz & U. Teichler (Eds.), The institutional basis of higher education research. Experiences and perspectives (pp. 45--55). London, UK: Kluwer Academic Publishers.Google Scholar
- Elo, S., & Kyngäs, H. (2007). The qualitative content analysis process, Journal of Advanced Nursing, 62, pp. 107--115.Google ScholarCross Ref
- Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5--6), 304--317. Google ScholarDigital Library
- Gasevic, D., Kovanovic, V., Joksimovic, S., & Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distributed Learning, 15(5), 134--176Google ScholarCross Ref
- Gašević, D., Dawson, S., & Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64--71.Google ScholarCross Ref
- Gilligan, C. (1982). In a different voice: Psychological theory and women's development. Cambridge, Mass: Harvard University Press.Google Scholar
- Godor, B. P. (2016). Academic fatalism: applying Durkheim's fatalistic suicide typology to student drop-out and the climate of higher education. Interchange, 1--13.Google Scholar
- Greller, W. & Drachsler, H. (2012). Translating Learning into Numbers: Toward a Generic Framework for Learning Analytics. Educational Technology and Society. 15 (3): 42--57Google Scholar
- Griffiths, D., Drachsler, H., Kickmeier-Rust, M., Hoel, T., & Greller, W. (2016). Is privacy a show-stopper for learning analytics? A review of current issues and solutions. Learning Analytics Review 6. LACE. Retrieved from http://www.laceproject.eu/learning-analytics-review/is-privacy-a-show-stopper/Google Scholar
- Herodotou, C., Rienties, B, Boroowa, A., Zdrahal, Z., Hlosta, M., Naydenova, G. (2016) Using Predictive Learning Analytics to Support Just-in-time Interventions: The Teachers' Perspective. To be submitted to Teaching and Teacher Education.Google Scholar
- Hsieh, H-F., & Shannon, S.E. (2005). Three approaches to qualitative content analysis, Qualitative Health Research, 15(9), 1277--1288.Google ScholarCross Ref
- Hülsmann, T. (2016). The impact of ICT on the costs and economics of distance education: A review of the literature. Retrieved from http://oasis.col.org/handle/11599/2047Google Scholar
- Hülsmann, T., & Shabalala, L. (2016). Workload and interaction: Unisa's signature courses - a design template for transitioning to online DE?. Distance Education, 37(2), 224--236.Google ScholarCross Ref
- Kitchen, R. (2014). The data revolution. Big data, open data, data infrastructures and their consequences. London, UK: Sage.Google Scholar
- Kogan, M., & Henkel, M. (2000). Future directions for higher education policy research. In S. Schwarz & U. Teichler (Eds.), The institutional basis of higher education research. Experiences and perspectives (pp. 25--43). London, UK: Kluwer Academic Publishers.Google Scholar
- Lincoln, Y. S., & Guba, E. G. (1990). Judging the quality of case study reports. International Journal of Qualitative Studies in Education, 3(1), 53--59.Google ScholarCross Ref
- Marshall, S., 2014. Exploring the ethical implications of MOOCs. Distance Education, 35(2), pp.250--262.Google ScholarCross Ref
- Massie, A. M. (2007). Suicide on campus: The appropriate legal responsibility of college personnel. Marq. L. Rev., 91, 625.Google Scholar
- McMahan, J. (1993). Killing, letting die, and withdrawing aid. Ethics, 103(2), 250--279.Google ScholarCross Ref
- Open University. (2014). Policy on ethical use of student data for learning analytics. Retrieved from http://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-use-of-student-data-policy.pdfGoogle Scholar
- Open University Tutor (Associate Lecturer) Support Statement (2012). Retrieved from http://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/tutor-support-statement.pdfGoogle Scholar
- Open University Terms and Conditions of Service for Associate Lecturers (2016). Retrieved from http://intranet6.open.ac.uk/student-services/main/sites/intranet6.open.ac.uk.student-services.main/files/files/ecms/web-content/associate-lecturers/al-services/AL-Terms-and-conditions-April-2016.pdfGoogle Scholar
- Patton, MC. 2008. Utilization-focused evaluation. {4th edition}. London, UK: Sage.Google Scholar
- Prinsloo (2016a). Evidence-based decision making as séance: implications for learning and student support. In Jan Botha & Nicole Muller (Eds.), Institutional Research in support of evidence-based decision-making in Higher Education in Southern Africa (pp. 331--353). Stellenbosch, South Africa: SUN Media.Google Scholar
- Prinsloo, P. (2016b, October 5). The increasing (im)possibilities of justice and care in open, distance learning. Presentation at EDEN Research Workshop, Oldenburg, Germany. Retrieved from http://www.slideshare.net/prinsp/the-increasing-impossibilities-of-justice-and-care-in-open-distance-learningGoogle Scholar
- Prinsloo, P., & Slade, S. (2014). Educational triage in higher online education: walking a moral tightrope. International Review of Research in Open Distributed Learning (IRRODL), 14(4), pp. 306--331. http://www.irrodl.org/index.php/irrodl/article/view/1881Google Scholar
- Prinsloo, P., & Slade, S. (2016). Here be dragons: Mapping student responsibility in learning analytics, in Mark Anderson and Collette Gavan (eds.), Developing Effective Educational Experiences through Learning Analytics (pp. 174--192). Hershey, Pennsylvania: ICI-Global.Google Scholar
- Rienties, B., Giesbers, B., Lygo-Baker, S., Ma, H.W.S & Rees, R. (2016). Why some teachers easily learn to use a new virtual learning environment: a technology acceptance perspective. Interactive Learning Environments, 24(3) pp. 539--552. Rule, P., & John, V. (2011). Case study research, Pretoria: Van Schaik Publishers.Google ScholarCross Ref
- Sclater, N. (2015, March 3). Effective learning analytics. A taxonomy of ethical, legal and logistical issues in learning analytics v1.0. JISC. Retrieved from https://analytics.jiscinvolve.org/wp/2015/03/03/a-taxonomy-of-ethical-legal-and-logistical-issues-of-learning-analytics-v1-0/Google Scholar
- Siemens, G., Irvine, V., & Code, J. (2013). Guest editors' preface to the special issue on MOOCs: an academic perspective on an emerging technological and social trend. Journal of Online Learning and Teaching, 9(2), iii--vi.Google Scholar
- Slade, S. & Prinsloo, P. (2013). Learning analytics: ethical issues and dilemmas. American Behavioral Scientist 57(1), 1509--1528.Google Scholar
- Schmidtz, D. (2000). Islands in a sea of obligation: limits of the duty to rescue. Law and Philosophy, 19(6), 683--705.Google Scholar
- Simpson, O. (2013). Supporting students in online open and distance learning. London, UK: Routledge.Google Scholar
- Sontag, D. (2002, April 28). Who was responsible for Elizabeth Shin? The New York Times. Retrieved from http://www.nytimes.com/2002/04/28/magazine/who-was-responsible-for-elizabeth-shin.htmlGoogle Scholar
- Spady, W. G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64--85.Google ScholarCross Ref
- Spady, W. G. (1971). Dropouts from higher education: Toward an empirical model. Interchange, 2(3), 38--62.Google ScholarCross Ref
- Subotzky, G., & Prinsloo, P. (2011). Turning the tide: a socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2): 177--19.Google ScholarCross Ref
- Tinto, V. (1975). Dropout from higher education: a theoretical synthesis of recent research. Review of Educational Research, 45(1), 89--125.Google ScholarCross Ref
- Thomas, G. (2011). How to do your case study. A guide for students and researchers, London, UK: Sage.Google Scholar
- Tyson, C. (2014, September 10). The 'murky' middle. Inside HigherEd. Retrieved from https://www.insidehighered.com/news/2014/09/10/maximize-graduation-rates-colleges-should-focus-middle-range-students-research-showsGoogle Scholar
- Velasquez, M., Andre, C., Shanks, T.S.J., & Meyer, M.J. (2015, August 1). Thinking ethically. Retrieved from https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/thinking-ethically/Google Scholar
- Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 1--21.Google Scholar
- Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., & Hlosta, M. (2014). Developing predictive models for early detection of at-risk students on distance learning modules, Workshop: Machine Learning and Learning. Presented at the Learning Analytics and Knowledge (2014), Indianapolis, Indiana, USA. Retrieved from https://pdfs.semanticscholar.org/3d16/f4008858795b126a351354101b9e378c9337.pdfGoogle Scholar
- World Bank. (2016). Digital dividends. Washington: International Bank for Reconstruction and Development / The World Bank. Retrieved from http://www.worldbank.org/en/publication/wdr2016.Google Scholar
Index Terms
- An elephant in the learning analytics room: the obligation to act
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