CFP: Library Trends: Learning Analytics and the Academic Library - Critical Questions About Real and Possible Futures

CFP: Library Trends: Learning Analytics and the Academic Library

Critical Questions About Real and Possible Futures

CFP URL: https://www.press.jhu.edu/journals/library-trends/calls-papers-special-issue

Guest editor: Kyle M. L. Jones
Abstract submission deadline: April 1, 2018
Publication date: March, 2019

Nature and scope of this issue:

Learning analytics is the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”1 If the academic library is the “most important observation post” for understanding how students learn, then it follows that libraries in colleges and universities should be a primary focus of data mining and analysis initiatives in higher education.2Integration of library data in learning analytics is fledgling at best, but there are growing calls for such activity to increase, especially to enhance a library’s ability to prove their resource expenditures and demonstrate alignment with wider institutional goals (e.g., improve learning outcomes, decrease costs, etc.).3

The efficacy of learning analytics is premised on an institution’s ability to identify, aggregate, and manage a wide variety and increasingly large volume of data about students, much of which needs to be identifiable in order to develop personalized, just-in-time learning interventions. So, in the fashion of other Big Data initiatives, institutions are beginning to dredge their information systems for student behaviors, personal information, and communications, all of which hold potential to reveal how students learn and uncover structural impediments to learning.

It is enticing to assume good things about library participation in learning analytics. The profession wants to provide just the right information at just the right time, and professional librarians want that information to aid students as they develop personally, academically, and professionally. Moreover, the profession seeks to further cement its position as a key player in the educational experience, and learning analytics may enable librarians to make stronger claims about their pivotal role once they gain access to new sources of data and the metrics that come from data analysis. But, like all technologies, learning analytics are not neutral; they are embedded with and driven by political agendas, which may not be congruent with—or necessarily aware of—extant values and ethical positions, such as those espoused by academic librarians and users of their libraries.4 Consequentially, scholars and practitioners need to take a critical approach to the growing role of learning analytics in academic libraries and the wider higher education context in order to better inform conversations concerning the intended and unintended positive and negative outcomes learning analytics can bring about.

This special issue is motivated by Neil Selwyn’s position that the “purposefulpursuit of pessimism” as it relates to educational technologies is constructive and fruitful.5 In contrast, optimism around emerging technologies—and the denial of critical voices—perpetuates a belief that technological progress is always a good thing. While we often perceive a pessimistic attitude towards technology as destructive or equate it to traditional Luddism, there is actually much to be gained by critically questioning the political agendas driving educational technology design, adoption, and diffusion.

This issue will invite authors to explore and push back against statements that learning analytics will somehow improve academic libraries by addressing questions around political positions and value conflicts inherent to learning analytics, coded in related information systems, and embedded in emerging data infrastructures.

List of potential topics

Potential articles may address these or related questions as the submitting author(s) believe to fit within the scope of the special issue:

• Who is pushing a learning analytics agenda, and are they able to exert power over others in ways that dominate personal and professional values?
• What economic model(s) are motivating the adoption of learning analytics, and how do these things restructure academic library work?
• What rights do 1) library users inherently hold as individuals situated in particular types of societies (Western democracies or otherwise), are 2) provided by policy and law, and 3) are potentially denied by academic library adoption of learning analytics technologies?
• How and in what ways does academic library participation in learning analytics contravene professional ethics and norms? Are there ways in which not participating might contravene other academic library values?
• Learning analytics surfaces personal behaviors and predilections by logging, aggregating, and providing access to user actions, but how might such practices not be justifiable?
• In what ways does academic library participation in learning analytics raise issues around intellectual freedom?
• What alternatives exist that can route around computationalism, so that other methods may be brought to bear on the wicked problems facing the academic library?
• We often assume that technologies will enhance social aspects of our lives, but how might learning analytics become a detriment to the user-librarian relationship?
• How might learning analytics be used as a managerial tool to evaluate and/or replace librarians’ expert labor, especially with regard to instruction and reference work?

Instructions for submission

The guest editor requests interested parties to submit an abstract of 500 words or less, following APA format for parenthetical and reference list citations, by April 1, 2018. Abstracts should be sent to kmlj@iupui.edu with the subject of “Library Trends: Abstract Submission.”

All submissions should follow the formatting requirements of the journal. Abstracts should include the author’s name, affiliation, and e-mail address. If more than one author is listed on the abstract, the guest editor will communicate with the first author only. The guest editor also requests that the author(s) includes an informal biography explaining how her/his past and present research and/or professional experience informs her/his submission.

In consultation with the editor of the journal, the guest editor will invite authors to submit full papers in early May, 2018. Full papers will be due to the guest editor by November 1, 2018; they will undergo a double-blind peer review. The guest editor is seeking qualified peer reviewers with expertise in the topic area (e.g., learning analytics, academic analytics, library analytics) and/or the theoretical area (e.g., critical data studies, information ethics and policy, STS). If you are interested in reviewing for the special issue, please contact the guest editor.

The journal expects to publish the issue in March, 2019.

Timeline
April 1, 2018	Abstract submissions due
May, 2018	Editors will notify author(s) if abstract is accepted
November 1, 2018	Article drafts due
October 1, 2018	Rolling peer review begins
January 1, 2019	Rolling peer review ends
January 15, 2019	Article decision announced
Jan.–Feb., 2019	Article revision period
February, 2019	Final articles due to journal editor for publication preparation
March, 2019	Special issue published

Information about the guest editor

Kyle M. L. Jones (MLIS, PhD) is an assistant professor within the School of Informatics and Computing (Department of Library and Information Science) at Indiana University–Indianapolis (IUPUI). His research focuses on the information ethics and policy issues associated with educational data mining tools, systems, and practices—such as learning analytics—in the context of higher education. You can find out more about his work at his website. He can be reached at kmlj@iupui.edu.

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1 Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. Proceedings of the Second International Conference on Learning Analytics and Knowledge, USA, 4–8. doi: 10.1145/2330601.2330605
2Duderstadt, J. J. (2009). Possible futures for the research library in the 21st century. Journal of Library Administration, 49(3), 217–225. doi: 10.1080/01930820902784770
3Connaway, L. S., Harvey, W., Kitzie, V., & Mikitish, S. (2017). Academic library impact: Improving practice and essential areas to research (Report). Retrieved from Association of College and Research Libraries website: http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/wh...
4Jones, K. M. L., & Salo, D. (forthcoming – 2018). Learning analytics and the academic library: Professional ethics commitments at a crossroads. College & Research Libraries. Available as a preprint at http://crl.acrl.org/index.php/crl/article/view/16603/18049