Rationale for the Call
Learning analytics as research focus and practice has promised to impact positively on the quality of teaching and learning, depending on, inter alia, having access to students’ digital data or, as the Call for Papers for the inaugural 2011 Learning Analytics and Knowledge Conference stated, the digital data that learners “throw off”. As well as access to students’ digital data, the effectiveness and appropriateness of interventions to support student learning may also be influenced by how student learning is understood, by the amount and quality of the collected data, as well as the quality of any subsequent analysis. These factors also impact on the scope, appropriateness of institutional responses to identified risks and/or learning behaviors.
What is often not considered are disparities in students’ access to sustainable and affordable connectivity and the subsequent impact of these disparities on the quality and scope of data provided, and flowing from this, on data analyses and institutional responses. There is a danger that students who have less access to sustainable and affordable connectivity may be doubly disenfranchised. Firstly given their reduced opportunities to engage and then as a result of a higher classification of being ‘at risk’ (due to lower levels of participation and engagement) which may marginalise them even further, often beyond the moment and/or context of being labelled ‘at risk’.
Our first question informing this Call for an Expression of Interest is to consider the notion of students’ ‘data poverty’ and the subsequent impact on effective, appropriate and ethical learning analytics and more generally, student support and success.
The notions of ‘data poor’ (referring to ‘not having access to data’) and ‘data poverty’ (the socio-material effects of being data poor) is more often associated with educational contexts in the Global South, or as a pertinent issue for the Global Majority. The recent pandemics have illustrated that data poverty is not only a problem among the Global Majority, but rather a global problem. Many individuals, social groups and communities are living off the grid, not because of choice, but as a result of geographic location, low income or exclusion based on other combinations in the intersectional spectrum. Given these often mutually constitutive characteristics and/or contextual factors, these individuals are “not being able to get a data contract, lack of privacy and local infrastructure” (Lucas, Robinson & Treacy, 2020), seriously hampering their access to the affordances of connected services and opportunities, and even survival in a networked society (Palmer, 2020). (Also see Hayes, 2021; Hayes, Connor, Johnson & Jopling, 2022; Palmer, 2016).
Milan and Treré (2020) refer to two data gaps corresponding with the notion of ‘data poverty’ – “The first gap concerns the data poverty perduring in low-income countries and jeopardising their ability to adequately respond to the pandemic. The second affects vulnerable populations within a variety of geopolitical and sociopolitical contexts, whereby data poverty constitutes a dangerous form of invisibility which perpetuates various forms of inequality”.
As the measurement, collection, analysis and use of student data attempts to make students’ learning visible, the invisibility of those students who are ‘data poor’ impacts not only on them, but also on the effectiveness of the resource allocation, pedagogical strategies and funding in/of their higher education institutions.
Our second question informing this Call for an Expression of Interest is to consider the notion of institutional ‘data poverty’ and the subsequent impact on effective, appropriate and ethical learning analytics. We invite submissions that will explore other nuances of data poverty, such as when institutions do not have the digital infrastructure, policies and capacity to measure, collect and analyse student data, and/or where the format (analogue and/or digital), quality and/or scope of student data which institutions have access to simply do not allow institutions to get a holistic picture of students’ learning. In these cases, data poverty refers to institutions being ‘data poor’.
The notion of institutional data poverty may include institutions who are data rich, but information and/or analysis poor (Spiker, 2014; Sticher, 2021) to which Goodman (2016) refers as the “Data rich, information poor syndrome”.
Expression of interest
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 on the impact of ‘data poverty’ in all of its variations as explained above in the rationale for this Call – whether referring to students access to data and the impact on learning analytics, or institutions lack of access to either quality and holistic data, or lack of digital infrastructure, resources and/or skills.
- This Call spans to separate, but linked research areas namely learning analytics, and the broader field of institutional research.
- Proposals should be very clear regarding their value contribution to the purpose of the Call for an Expression of Interest. Successful proposals will add to our understanding of the specific challenges/potential of learning analytics and/or the broader context of institutional research.
- Conceptual, theoretical, qualitative, quantitative or mixed methods approaches are welcome
We sincerely hope to find an Open Publisher for this book if we can obtain funding (in process), but if not possible, we will consider other reputable academic publishers
Who we are
Paul Prinsloo is a Research Professor in Open and Distance Learning (ODL) in the Department of Business Management, College of Economic and Management Sciences, University of South Africa (Unisa). He is a Visiting Professor at the Carl von Ossietzky University of Oldenburg, Germany, a Research Associate for Contact North I Contact Nord (Canada), a Fellow of the European Distance and E-Learning Network (EDEN), member of the Executive Committee for the Society of Learning Analytics Research (SoLAR) and serves on several editorial boards. In the South African context Paul has a B3 research rating confirming his considerable international reputation for the high quality and impact of his research outputs. 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, the ethical collection, analysis and use of student data in learning analytics, and digital identities.
He blogs at https://opendistanceteachingandlearning.wordpress.com/ and his Twitter/Mastodon alias is @14prinsp
Dr Sharon Slade worked in open, distance education for almost 20 years, as a senior lecturer at the Open University in the UK. She led the work on development of the University’s Policy on the ethical use of student data for learning analytics, arguably the first of its kind in Higher Education worldwide. She has since contributed to further framework developments, notably with Jisc, Stanford University and Ithaka S+R, New America and the International Council for Open and Distance Education. Sharon was an academic lead for learning analytics projects within the Open University, leading work around ethical uses of student data, operationalisation of predictive analytics and approaches aiming to improve retention and progression. Keynotes and publications include papers 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. She now works on data insight at Earth Trust, an educational and environmental charity near Oxford.
Dr. Mohammad Khalil is a senior researcher and lecturer in learning analytics at the Centre for the Science of Learning & Technology (SLATE) within the Faculty of Psychology at the University of Bergen in Norway. Khalil, as an Erasmus Mundus granted scholar, has a Ph.D. with distinction from Graz University of Technology in Learning Analytics in Massive Open Online Courses (MOOCs). He worked as a Postdoc at Delft University of Technology for almost 2 years in Technology Enhanced Learning. Khalil leads several international projects in learning analytics, including Erasmus+ and PederSather. Khalil is currently an associate editor of the International Journal of Emerging Technologies in Learning (iJET) and a Norwegian representative for Standard Norway to the European standardisation committee (CEN/TC 353) on MOOCs. His key research interests include Technology Enhanced Learning, AI in Education, Educational Data Mining, and Privacy and Ethics.
Proposal Submission Details
Proposal length should be maximum 600 words, not including references
Chapter length should be a maximum of 7,000 words, including references
Submissions will be:
- submitted in Microsoft® Word
- in English
- double-spaced, in 12-point font, Arial or Times New Roman
- using APA style referencing
- original, not previously published, not submitted for publication elsewhere, and not revised from a previous submission elsewhere
- Authors of accepted chapter proposals may be asked to review two other submissions
Format details to follow upon proposal acceptance
Submissions should be submitted to email@example.com
|January 23, 2023||First Call for Expressions of Interest|
|February 28, 2023||Second Call for Expressions of Interest|
|March 13, 2023||Final Call for Expressions of Interest|
|May 1, 2023||Proposal deadline|
|May 15, 2023||Notification of Provisional Acceptance|
|July 19, 2023||Full Chapter Submission|
|August 28, 2023||Review Results ReturnedChapters provisionally selected|
|October 2, 2023||Resubmission of provisionally accepted submissions|
|November 6, 2023||Reviews returned|
|December 4, 2023||Final submission of chapters|
|January 8, 2024||Submission of final manuscript to publisher|
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.
Goodman, S. (2016). Data rich, information poor (DRIP) syndrome: is there a treatment?
Retrieved from https://pubmed.ncbi.nlm.nih.gov/10158370/
Hayes, S. (2021). Postdigital positionality. Developing powerful inclusive narratives for learning, teaching, research and policy in higher education. Brill Publishing.
Hayes, S., Connor, S., Johnson, M., & Jopling, M. (2022). Connecting cross-sector community voices: Data, disadvantage, and postdigital inclusion. Postdigital Science and Education, 4(2), 237-246.
Lucas, P.J., Robinson, R., & Treacy, L. (2020). What is data poverty? NESTA. Retrieved from https://www.nesta.org.uk/report/what-data-poverty/
Milan, S., & Treré, E. (2020). <? covid19?> The Rise of the Data Poor: The COVID-19 Pandemic Seen From the Margins. Social Media+ Society, 6(3), 2056305120948233.
Palmer, S. (2016, May 1). Rich Data, Poor Data: What the Data Rich Do – That the Data Poor and the Data Middle Class Do Not! Retrieved from https://www.shellypalmer.com/2016/05/rich-data-poor-data-data-rich-data-poor-data-middle-class-not/
Palmer, S. (2020, March 11). Can the data poor survive? Straight Talk. Retrieved from https://straighttalk.hcltech.com/blogs/can-the-data-poor-survive
Spiker, S. (2014, November 24). Data rich, analysis poor. Retrieved from https://www.govloop.com/community/blog/data-rich-analysis-poor/
Stichter, A. (2021, June 16). Data rich and information poor. IIoT World. Retrieved from https://iiot-world.com/industrial-iot/connected-industry/data-rich-and-information-poor/