An elephant in the learning analytics room: the obligation to act

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.

[1]  David Stuart,et al.  The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences , 2015, Online Inf. Rev..

[2]  William G. Spady,et al.  Dropouts from higher education: An interdisciplinary review and synthesis , 1970 .

[3]  Paul Prinsloo,et al.  Here Be Dragons: Mapping Student Responsibility in Learning Analytics , 2016 .

[4]  James E. Willis,et al.  Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective , 2016 .

[5]  J. Creswell Qualitative inquiry and research design: Choosing among five approaches, 2nd ed. , 2007 .

[6]  D. Schmidtz Islands in a Sea of Obligation: Limits of the Duty to Rescue , 2000 .

[7]  John M. Braxton Reworking the Student Departure Puzzle , 2020 .

[8]  Academic Fatalism: Applying Durkheim’s Fatalistic Suicide Typology to Student Drop-Out and the Climate of Higher Education , 2017 .

[9]  Hendrik Drachsler,et al.  Privacy and analytics: it's a DELICATE issue a checklist for trusted learning analytics , 2016, LAK.

[10]  Thomas Hulsmann The Impact of ICT on the Costs and Economics of Distance Education: A Review of the Literature , 2016 .

[11]  U. Teichler,et al.  The Institutional Basis of Higher Education Research , 2002 .

[12]  Zdenek Zdráhal,et al.  Developing predictive models for early detection of at-risk students on distance learning modules , 2014, LAK Workshops.

[13]  Carol Calvert Developing a model and applications for probabilities of student success: a case study of predictive analytics , 2014 .

[14]  Hendrik Drachsler,et al.  Translating Learning into Numbers: A Generic Framework for Learning Analytics , 2012, J. Educ. Technol. Soc..

[15]  Laura E. Rumbley,et al.  Trends in Global Higher Education: Tracking an Academic Revolution: A Report Prepared for the UNESCO 2009 World Conference on Higher Education , 2010 .

[16]  Rebecca Ferguson,et al.  Learning analytics: drivers, developments and challenges , 2012 .

[17]  Paul Prinsloo,et al.  Educational Triage in Open Distance Learning: Walking a Moral Tightrope. , 2014 .

[18]  George Siemens,et al.  Let’s not forget: Learning analytics are about learning , 2015 .

[19]  C. Gilligan In a Different Voice: Psychological Theory and Women’s Development , 2009 .

[20]  George Siemens,et al.  Where is research on massive open online courses headed? A data analysis of the MOOC research initiative , 2014 .

[21]  References , 1971 .

[22]  Hsiu-Fang Hsieh,et al.  Three Approaches to Qualitative Content Analysis , 2005, Qualitative health research.

[23]  William G. Spady,et al.  Dropouts from higher education: Toward an empirical model , 1971 .

[24]  D. DavisFred,et al.  User Acceptance of Computer Technology , 1989 .

[25]  Lynn Westbrook,et al.  Utilization-focused evaluation , 1998 .

[26]  P. Prinsloo,et al.  Educational triage in higher open education: walking a moral tightrope , 2014 .

[27]  A Botes,et al.  A comparison between the ethics of justice and the ethics of care. , 2000, Journal of advanced nursing.

[28]  Paul Prinsloo,et al.  Turning the tide: a socio-critical model and framework for improving student success in open distance learning at the University of South Africa , 2011 .

[29]  John W. Creswell,et al.  Qualitative Inquiry and Research Design: Choosing Among Five Approaches , 1966 .

[30]  Ormond Simpson,et al.  Supporting Students in Online, Open and Distance Learning , 2000 .

[31]  Yvonna S. Lincoln,et al.  Judging the quality of case study reports , 1990 .

[32]  Stephen Marshall,et al.  Exploring the ethical implications of MOOCs , 2014 .

[33]  Wilfried Bos,et al.  Content analysis in empirical social research , 1999 .

[34]  H. Drachsler,et al.  Privacy and Learning Analytics - it's a DELICATE issue , 2016 .

[35]  A. Schmid How To Do Your Case Study A Guide For Students And Researchers , 2016 .

[36]  Bart Rienties,et al.  Why some teachers easily learn to use a new virtual learning environment: a technology acceptance perspective , 2016, Interact. Learn. Environ..

[37]  A. Massie Suicide on Campus: The Appropriate Legal Responsibility of College Personnel , 2007 .

[38]  Helvi Kyngäs,et al.  The qualitative content analysis process. , 2008, Journal of advanced nursing.

[39]  M. Henkel,et al.  Future Directions for Higher Education Policy Research , 2000 .

[40]  Thomas Hülsmann,et al.  Workload and interaction: Unisa’s signature courses – a design template for transitioning to online DE? , 2016 .

[41]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[42]  P. Webb,et al.  How to do your case study: a guide for students and researchers , 2014 .

[43]  P. Prinsloo,et al.  Learning Analytics , 2013 .

[44]  Rob Kitchin,et al.  The data revolution : big data, open data, data infrastructures & their consequences , 2014 .

[45]  Jeff McMahan,et al.  Killing, Letting Die, and Withdrawing Aid , 1993, Ethics.

[46]  L. Alexander Lesser Evils: A Closer Look at the Paradigmatic Justification , 2005 .

[47]  Vincent Tinto Dropout from Higher Education: A Theoretical Synthesis of Recent Research , 1975 .

[48]  George Siemens,et al.  Guest Editors' Preface to the Special Issue on MOOCs An Academic Perspective on an Emerging Technological and Social Trend , 2013 .