Special Issue Call for Papers: From Learning Analytics to Institutional Learning Ecosystems
Aims & Scope of Special Issue
What kind of model should institutions of higher education (IHEs) use as they look to utilize information drawn from the learning activities conducted with digital platforms for systemic improvement? Among the impacts of the COVID-19 pandemic is the realization that instructional support technology can be used on a massive scale when needed. Platform-based teaching provides options for IHEs to respond to a range of needs, to be resilient in the face of other disasters and disruptions that may occur in the future. Platforms can also allow an IHE to extend its reach to new communities. The phrase institutional learning ecosystems borrows concepts from organizational learning to advance a broad view of how an organization connects to its stakeholders. The ecosystem framing is a contemporary approach used with communities and complex organizations—terms that could apply to IHEs—that highlights emergent, adaptive, and responsive approaches (Wang, 2021).
This special issue will provide fresh perspectives joining other work questioning what kinds of research to value in a post-pandemic world. Reeves and Lin (2020) suggest technology researchers focus less on things and more on problems of education. Chinn, Barzilai, & Duncan (2020) argue for educational research that connects to epistemic cognition, including “understanding of performance, caring and enjoyment, and participation in performance with others.” (p 51). Like these forward-looking pieces, this issue will consider data collected for analysis from student learning technologies—learning analytics—with a sociotechnical lens to understand how these data and interpretive practices can be levers on system-wide challenges, including equity and inclusion.
University of Maryland College Park
Tutaleni I. Asino
Oklahoma State University
Kyle M. L. Jones
Indiana University-Indianapolis (IUPUI)
Patricia A. Young
University of Maryland Baltimore County
Topics of Interest
Topics may include but are not limited to:
● Intersection of learning analytics and institutional research
● Studies focusing on equitable access to educational opportunities
● Understanding different student experiences through their digital traces
● Learning analytics for social justice and ethics
● Data visualization technologies
● Institutional resilience with technology
● Combining Learning Management System (LMS) data and administrative records
● Indicator systems for higher education management
● Culture in IHE information systems
● New approaches to defining excellence in higher education
● Data to understand educational pipelines and transitions
● Connecting institutional stakeholders around information practices
● Learner diversity, diverse learner needs
● Information architecture in higher education
Authors interested in a topic they believe is related though not listed are encouraged to contact Phil Piety (firstname.lastname@example.org) to discuss possible fit.
Survey for Interest in Reviewing, Submissions, and Topics
With this special issue, the guest editors have decided to include an interest survey: https://iu.co1.qualtrics.com/jfe/form/SV_0dgJfTgbakA6b5k
Please feel free to provide information through this link and to forward it to colleagues that may be interested in these topics.
Kinds of Submissions
In moving beyond a traditional institutional view of higher education and including the concepts of ecosystems and resilience, this issue is inviting different conceptions of what educational organizations can be. Papers are encouraged that address IHEs as communities reflecting core values of diversity, equity, inclusion, and justice in the kinds of data collected and how the resulting information is analyzed and used. Empirical papers are encouraged where there is research that supports evidence-based inferences. Theoretical pieces will also be considered. Priority will be given to papers that demonstrate a strong grounding in theory and/or appropriate research design. Methods can be quantitative, qualitative, design-based, or mixed methods. Case studies are included. Systematic reviews may be considered if they are clearly aligned with the special issue and outline interesting challenges and research opportunities in this area. One or two position papers may be considered if they are well-written, well-resourced, and advance significant new ideas or conceptual frameworks for this topic.
This special issue seeks original contributions that focus on these connections between the learning side and the organizational side of higher education. The issue is intended to describe a future of higher education where information drawn from the instructional core of education activities (Elmore, 2004) can be used to connect to the larger institutional and systemic goals (Baker & Inventado, 2014) and support continuous improvement processes (O’Neill & Palmer, 2004). Articles that can make connections between instructional needs and the design and planning of systems are encouraged. Submissions should help focus attention on core challenges that IHE may be face in reaching this future. Submissions should have a broad perspective, including using information to drive needs assessments and support the conversation about core missions.
Background: Learning Analytics, Institutional Learning, and Resilience
As in other sectors of education, recent years have seen an explosion of research and development in the use of data that have been drawn from digital technologies in higher education. While several terms for higher education educational data use have gained currency in recent years, learning analytics has become widely accepted. Building on the collection of digital data in admissions and administrative systems that has been ongoing for many decades (Reichard, 2012), the emergence of learning analytics was tied to the online tools used to support learning (Clow, 2013; Ferguson, 2012; Piety, Hickey, and Bishop, 2014; Siemens, 2013). It has raised new possibilities about how to connect the copious amounts of detailed interactional data the tools provide to broader institutional mission and support organizational learning (Bui & Baruch 2010)?
The COVID-19 pandemic plays an important role in the importance of this special issue. It is well known that the public health circumstances of the pandemic caused a whole generation of youth and faculty to experience online instruction as a daily reality. The pandemic also highlighted some challenges different groups face and the importance of designing instructional technologies to support different cultural dimensions (Young, 2008). Beyond the obvious increase in distance education tools, these circumstances highlight the need for a resilient approach to higher education. Resilience includes the institution’s capacity to shift resources in response to different challenges from biological, man-made, and natural disasters that can strike any community at any time.
The following table shows target dates for the special issue development cycle
November 31, 2021
Manuscript proposals (2 pages) due
January 15, 2022
Feedback on the proposal and manuscript invitation
May 31, 2022
Full manuscripts due
August 31, 2022
Notification of reviewers’ 1st feedback
October 31, 2022
Revised manuscript submission
December 31, 2022
Feedback on revised manuscripts
February 28, 2023
Final manuscript submission
May 31, 2023
Notification of final decision
July 31, 2023
Special issue publication
Proposals are two pages or approximately 500-1000 words excluding references. Proposals should indicate the kind of paper (ex: research, critique, theoretical) and what kinds of resources (ex: data, conceptual lenses, etc.) will be used. Proposers should clearly describe the contribution the paper will make to understanding and advancing the use of information for IHE systemic improvement.
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer, New York, NY.
Bui, H., & Baruch, Y. (2010). Creating learning organizations in higher education: applying a systems perspective. The Learning Organization.
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695.
Chinn, C. A., Barzilai, S., & Duncan, R. G. (2020). Education for a “post-truth” world: New directions for research and practice. Educational Researcher, 0013189X20940683.
Elmore, R. F. (2004). School reform from the inside out: Policy, practice, and performance. Harvard Education Press. 8 Story Street First Floor, Cambridge, MA 02138.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317.
Howard, R. D., McLaughlin, G. W., & Knight, W. E. (2012). The handbook of institutional research. John Wiley & Sons.
O’Neill, M. A., & Palmer, A. (2004). Importance‐performance analysis: a useful tool for directing continuous quality improvement in higher education. Quality assurance in education.
Penuel, B. (2020, May). The new normal could be better than the old one. A learning scientist explains why. National Education Policy Center. https://nepc.colorado.edu/publication/newsletter-penuel-052120
Piety, P. J., Hickey, D. T., & Bishop, M. J. (2014, March). Educational data sciences: Framing emergent practices for analytics of learning, organizations, and systems. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 193-202).
Piety, P. J. (2019). Components, Infrastructures, and Capacity: The Quest for the Impact of Actionable Data Use on P–20 Educator Practice. Review of Research in Education, 43(1), 394-421.
Reeves, T. C., & Lin, L. (2020). The research we have is not the research we need. Educational Technology Research and Development, 68(4), 1991-2001.
Reichard, Donald J. (2012). The History of Institutional Research. The Handbook of Institutional Research (pp. 8–11). San Francisco, CA: Jossey-Bass.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
Young, P. A. (2008). Integrating culture in the design of ICTs. British Journal of Educational Technology, 39(1), 6-17.
Wang, P. (2021). Connecting the Parts with the Whole: Toward an Information Ecology Theory of Digital Innovation Ecosystems. MIS Quarterly, 45(1), 397-422.
Zilvinskis, J., Willis III, J., & Borden, V. M. (2017). An overview of learning analytics. New Directions for Higher Education, 2017(179), 9-17.