Learning analytics in open, distance and distributed learning: Potential, practice and challenges


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As part of the series, Springer Briefs in Open and Distance Education, we invite expressions of interest for a book that will critically explore and map the potential and challenges of learning analytics in the specific context of open, distance and distributed learning. Since the emergence of learning analytics as a phenomenon, research focus and practice, the field has matured into a rich, interdisciplinary field and praxis (Ferguson, 2012; Wong & Li, 2019). Not only has learning analytics as research and practice matured, but it has also come to terms with its imperfections, and to some extent, lack of evidence that it positively contributes to student success (Ferguson & Clow, 2017; Kitto, Shum, & Gibson, 2018, March). There is, however, evidence of how learning analytics shape pedagogy, student retention strategies and the strategic allocation of institutional resources (Gašević, Dawson & Siemens, 2015; Leitner, Khalil & Ebner, 2017; Lim, et al., 2019). Currently, much of the research, whether empirical, theoretical or conceptual, originates from residential or traditional forms of educational delivery.

Distance education as a unique form of educational delivery has evolved from correspondence education to various possibilities including offline, digitally supported, internet supported and fully online learning (Evans & Nations, 1992; Guri-Rosenblit, 2009; Holmberg, 2005; Moore & Kearsly, 2012; Peters, 2004, 2010). Within the scope of distance education, we not only have Open Distance Learning (ODL), as a particular form of distance education, but also various other forms of distributed online/blended learning such as Massive Open Online Courses (MOOCs), and fully online course offerings on traditional campus-based higher education. These different forms of distance education all share, in one way or the other, the characteristic of students being detached from the delivering institution with regard to space, place and time. Being separated from the delivering institution allows students flexibility and choice, but it also may result in feelings of isolation, lack of peer support and access to on-campus resources. As such, open and distance education institutions are often left in the dark regarding how students are progressing in their courses, and what support students may need at specific junctures in their learning journeys.

While distance education institutions have always collected and analysed student data, this mostly informed operational and strategic planning, resource allocation and was used for reporting to a variety of stakeholders. As such, the collection, analysis and use of student data in much of open, distance and distributed forms of educational delivery fall in the category of academic analytics, and not learning analytics. [For a discussion on the difference between academic and learning analytics see Siemens (2013)]. Learning analytics as the measurement, collection, analysis and use of student data (demographic and behavioral) has therefore the potential to not only inform pedagogy and student learning, as well as the appropriate allocation of resources. Learning analytics focus on improving student learning and to positively impact on student retention and success. In the light of historical and persisting concerns about student retention and success in open and distance learning (Subotzky & Prinsloo, 2011), collecting, analysing and using student data may assist in more appropriate pedagogical strategies and practices, materials, assessment (both formative and summative), resource allocation and planning, and contribute to student retention and success.

Institutions not only have access to a greater volume, variety and granularity of student data than ever before (e.g. Kitchin, 2014), but developments in Artificial Intelligence (AI), machine learning and neural networks open up not only new opportunities for the analysis and use of student data, but also raise particular concerns pertaining to, for example, the potential of bias, the ‘black box’ of algorithmic decision-making and the lack of oversight and accountability (Prinsloo, 2017; Vytasek, Patzak & Winne, 2020). In the context of open, distance and distributed learning environments, these developments pose huge potential to scale student support and learning, provide real-time advice and personalised learning journeys and as such, to break or ameliorate the impact of the iron triangle of cost, quality and access in distance, open and distributed learning (Daniel, Kanwar, & Uvalić-Trumbić, 2009; Power & Morven-Gould, 2011). We also have to critically consider the potential for harm and prejudice these new developments pose for students who may already be at risk. (See, for example, Macgilchrist, 2019; Pardo & Siemens, 2014; Prinsloo, Khalil & Slade, 2018; Slade & Prinsloo, 2013; Weber, 2016).

We invite researchers, scholars and practitioners to submit proposals for chapters using the following as guidelines:

  • The purpose of this book is to specifically address the current gap of published/reported theoretical, conceptual and empirical research focusing on learning analytics in open, distance and distributed environments. Preference will be given to researchers, scholars or practitioners working in open, distance and distributed learning contexts
  • Specific reference to theories and research in open, distance and distributed learning contexts
  • We believe that the specific nature of open, distance and distributed learning offers unique challenges but also unique potential to learning analytics as research focus, but also as practice/praxis
  • Proposals should be very clear regarding their value contribution to the purpose of the call for expression of interest. Successful proposals will add to our understanding of the specific challenges/potential of learning analytics in open, distance and distributed learning.
  • Conceptual, theoretical, qualitative, quantitative or mixed methods approaches are welcome
  • Of specific interest is for conceptual, theoretical or empirical research on the use of Artificial Intelligence (AI), machine learning and neural networks in open, distance and distributed learning contexts
  • We prefer chapters not to have more than two authors

 Who we are

 Paul Prinsloo

Paul Prinsloo is a Research Professor in Open and Distance Learning (ODL) in the Department of Business Management, in the College of Economic and Management Sciences, University of South Africa (Unisa). Since 2015, he is also a Visiting Professor at the Carl von Ossietzky University of Oldenburg, Germany. In 2019, the National Research Foundation (NRF) in South Africa awarded Paul with a B3 rating confirming his considerable international reputation for the high quality and impact of his research outputs. He is also a Fellow of the European Distance and E-Learning Network (EDEN) and serves on several editorial boards. His academic background includes fields as diverse as theology, art history, business management, online learning, and religious studies. Paul is an internationally recognised speaker, scholar and researcher and has published numerous articles in the fields of teaching and learning, student success in distance education contexts, learning analytics, and curriculum development. His current research focuses on the collection, analysis and use of student data in learning analytics, graduate supervision and digital identity.

 Sharon Slade

 Sharon Slade is a senior lecturer at the Open University in the UK with a background in mathematical modelling.  She is an academic lead for learning analytics projects within the University, leading work around ethical uses of student data, operationalisation of predictive analytics and approaches aiming to improve retention and progression.  Recent contributions include papers and chapters around student consent, the obligation to act on what is known, examining the concept of educational triage and broader issues around an ethics of care.

 Mohammad Khalil

Mohammad Khalil is a senior researcher and lecturer in learning analytics at the Centre for the Science of Learning & Technology (SLATE), in the department of psychology, University of Bergen, Norway. Mohammad has a Ph.D. from Graz University of Technology in Learning Analytics in Massive Open Online Courses (MOOCs). He has 2 years’ experience at the Centre for Education and Learning from Delft University of Technology, the Netherlands. In 2019, Mohammad becomes the Norwegian representative at the European standards for MOOC quality (SN/K 186). Mohammad has published many articles on learning analytics in high-standard journals and conferences. His current research focuses on learning analytics in Open and Distance Learning (ODL), health, mobile, visualizations and gamification, as well as privacy and ethics.

 Submission Details

Only eight (8) chapters, each with a maximum of 5,000 words will be accepted.

Proposal length should be maximum 600 words, not including references

Chapter length should be a maximum of 5,000 words, including references

Submissions will be:

  • submitted in Microsoft® Word
  • in English
  • double-spaced, in 12-point font
  • using APA style referencing
  • original, not previously published, not submitted for publication elsewhere, and not revised from a previous submission elsewhere

Peer review

  •  Double-blind
  • Authors of accepted chapter proposals will be asked to review two other submissions

Format details to follow upon proposal acceptance

Submissions should be submitted to prinsp@unisa.ac.za

Important dates

June 30, 2019 Proposal Submission Deadline
July 15, 2019 Notification of  Provisional Acceptance
November 29, 2019 Full Chapter Submission
January 31, 2020 Review Results Returned

Final Selection of 8 (eight) Chapters

March 31, 2020 Resubmission of Accepted Submissions
April 18, 2020 Final Acceptance Notification
April 30, 2020 Submission of Manuscript to Springer

Target audience

We anticipate that a varied audience for this publication will include a range of individuals in or having an interest in open, distance and distributed learning environments, including but not limited to managers, policymakers, instructors, academic planners, ICT, faculty, financial managers, researchers, practitioners, curriculum developers, instructional designers, and  administrators. The open-access publication of this book will increase potential readership.

References

Daniel, J., Kanwar, A., & Uvalić-Trumbić, S. (2009). Breaking higher education’s iron triangle: Access, cost, and quality. Change: The Magazine of Higher Learning, 41(2), 30-35.

Evans, T., & Nations, D. (1992). Theorising open and distance education. Open Learning, 7(2), 3-13.

Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304-317.

Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. In: LAK ’17 Proceedings of the Seventh International Learning Analytics and Knowledge Conference, ACM International Conference Proceeding Series, ACM, New York, USA, pp. 56–65.

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.

Guri-Rosenblit, S. (2009). Diverse models of distance teaching universities. Encyclopedia of Distance Learning, 2, 727-733.

Holmberg, B. (2005). The evolution, principles, and practice of distance education. Oldenburg, Germany: BIS-Verlag der Carl von Ossietzky Universität Oldenburg (pp. 37-88, 104-105).

Moore, M. G., & Kearsley, G. (2012). Distance education: A systems view of online learning USA: Wadsworth-Cengage Learning.

Peters, O. (2010). Distance education in transition: Developments and issues (5th edition). Oldenburg, Germany: BIS-Verlag der Carl von Ossietzky Universität Oldenburg.

Power, T. M., & Morven-Gould, A. (2011). Head of gold, feet of clay: The online learning paradox. The International Review of Research in Open and Distributed Learning, 12(2), 19-39.

Prinsloo, P. (2017). Fleeing from Frankenstein’s monster and meeting Kafka on the way: Algorithmic decision-making in higher education. E-Learning and Digital Media, 14(3), 138-163.

Prinsloo, P., Khalil, M., & Slade, S. (2018). User consent in MOOCs – micro, meso, and macro perspectives. International Review of Research in Open and Distributed Learning (IRRODL), 19(5), 61-79.

Kitchen, R. (2014). The data revolution. Big data, open data, data infrastructures and their consequences. London: Sage.

Kitto, K., Shum, S. B., & Gibson, A. (2018, March). Embracing imperfection in learning analytics. Paper presented at LAK ’18, Sydney, Australia. Retrieved from http://users.on.net/~kirsty.kitto/papers/embracing-imperfection-learning-final.pdf

Leitner, P., Khalil, M., & Ebner, M. (2017). Learning analytics in higher education – a literature review. In A. Peña-Ayala (Ed.), Learning Analytics: Fundamentals, Applications, and Trends (pp.1-23). Springer, Cham.

Lim, L. A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2019). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2019.04.003

Macgilchrist, F. (2019). Cruel optimism in edtech: When the digital data practices of educational technology providers inadvertently hinder educational equity. Learning, Media and Technology, 44(1), 77-86.

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450.

Peters, O. (2004). The iceberg has not yet melted: Further reflections on the concept of industrialization and distance teaching. In O. Peters, Distance education in transition: Developments and issues (5th edition), (pp. 33-42). Oldenburg, Germany: BIS-Verlag der Carl von Ossietzky Universität Oldenburg.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.

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 Education32(2), 177-193.

Vytasek, J. M., Patzak, A., & Winne, P. H. (2020). Analytics for student engagement. In A.S. Lampropoulos & Tsihrintzis, G.A. (Eds), Machine Learning Paradigms: Applications in Recommender Systems (pp. 23-48). New York, NY: Springer.

Weber, A. S. (2016). The big student big data grab. International Journal of Information and Education Technology, 6(1), 65-70.

Wong, B. T. M., & Li, K. C. (2019). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 1-22.

 

 

 

 

 

 

 

 

About opendistanceteachingandlearning

Research professor in Open Distance and E-Learning (ODeL) at the University of South Africa (Unisa). Interested in teaching and learning in networked and open distance and e-learning environments. I blog in my personal capacity and the views expressed in the blog does not reflect or represent the views of my employer, the University of South Africa (Unisa).
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