=Paper=
{{Paper
|id=Vol-1915/paper7
|storemode=property
|title=Building the Learning Analytics Curriculum
|pdfUrl=https://ceur-ws.org/Vol-1915/paper7.pdf
|volume=Vol-1915
|authors=Charles Lang,Stephanie Teasley,John Stamper
|dblpUrl=https://dblp.org/rec/conf/lak/LangTS17a
}}
==Building the Learning Analytics Curriculum==
https://ceur-ws.org/Vol-1915/paper7.pdf
Building the Learning Analytics Curriculum
Charles Lang
Teachers College, Columbia University
Stephanie Teasley
School of Information, University of Michigan
John Stamper
Human-Computer Interaction Institute, Carnegie Mellon University
Abstract
Learning Analytics courses and degree programs both on- and offline have begun to
proliferate over the last three years. As a result of this growth in interest from students,
university administrators, researchers and instructors a workshop was convened at the
Learning Analytics and Knowledge Conference 2017 in Vancouver, Canada. The broad
aim of the workshop was to review how these educational efforts are impacting the
field, how synergy between instructors might be developed to greater serve the field
and what kinds of best practices could be developed. In the following summary we
discuss themes that emerged as a result of this effort.
Background
Learning Analytics courses and degree programs both on- and offline have begun to
proliferate over the last three years. Tens of thousands of students registered for online
courses such as DALMOOC [1] and BDEMOOC [2], there has been considerable
excitement around the growth of new degree programs such as the MSc in Learning
Analytics at Teachers College, Columbia [3] and the strong analytics focus of the MSc in
Digital Education at the University of Edinburgh [4], as well as an explosion of individual
courses including those at the University of Michigan [5], The University of South
Australia [6] and others. These efforts are building on the longstanding teaching of
influential material at Carnegie Mellon University and the lasting success of the Learning
Analytics Summer Institutes [7] as well as professionally-minded projects such as LACE
[8]. As such, the growth of the field is clearly happening not just through research, but
through the classroom. As such LAK17 seemed a good time to review how these
educational efforts are impacting the field of Learning Analytics, create a forum to
discuss the unique challenges of teaching Learning Analytics and share best practices to
strengthen educational offerings in the future.
Workshop Objectives
The audience for the workshop were those engaged in the work of developing learning
analytics curricula and programming, teaching learning analytics courses, administering
learning analytics programs or otherwise interested in the professionalization of the
field. The overall objectives of the workshop were to discuss practice and share
resources for the formal teaching of Learning Analytics. There were four key sections:
The Many Goals of LA Program Development
The goals of Learning Analytics programs and courses are many and varied. From
providing an introduction to the field for the general public to training people for
particular roles within the burgeoning employment market for Learning Analysts (or
whatever these individuals come to be called). The objective of this section of the
workshop was to consider and discuss the underlying purpose of learning analytics
programming at both the program and course level. Ryan Baker lead a discussion of the
challenges in setting up learning analytics programs within higher education institutions
and provided context around how course offerings influence the field overall, and how
to balance the desires of different stakeholders such as employers, university
administrators and researchers in defining the expertise that graduates should hold.
Justin Dellinger gave a similar presentation about setting up micro-masters credentials
within the MOOC format.
Course Content & Sequence
The second objective of the workshop was to discuss and develop ideas around content
and the sequence in which that content should be taught. Discussions were held around
determining the level of consensus that exists within the community about what
content should be included within Learning Analytics programming and what the
priorities across different cultural, geographic, research paradigms and educational
settings might be. Leah MacFadyen lead a discussion on how this dialogue has evolved
over the last five years and where it may go in the future.
Sharing Tools & Resources
The third aim of the workshop was to demonstrate useful tools and resources for
teaching Learning Analytics. John Stamper gave an overview of advances in data sharing
systems for teaching purposes including the development of LearnSphere.
Incorporating LA Practice into Course Program Offerings
Possibly the most challenging part of teaching Learning Analytics is ``walking the talk``,
or in other words, thinking through how to incorporate Learning Analytics into the
courses we teach and programs we run. In many cases, the courses taught by
researchers become the first attempts at observing learning analytics in the wild but
whether this ad hoc approach can be systematized remains to be seen. Although
participants agreed this was a desirable aim there was little being done to implement it
in the classroom. Both Vitomir Kovanovic and Xu Wang offered preliminary ideas about
how this might occur.
Best Practices
We hope to push forward the conversation around strategies to codify teaching
practices. To develop an understanding amongst practitioners as to what works and
what does not work with respect to teaching Learning Analytics. To that end Paul
Prinsloo and Sharon Slade provided a commentary on whether or not a code of ethics
should be taught as part of learning analytics programming. We hope that this will
develop a lasting conversation that can propel the field forward in the most effective,
impactful and rigorous way.
References
[1] G. Siemens, C. Rose, D. Gasevic, and R. Baker, “Data, Analytics and Learning,” edX,
02-Jun-2014. [Online]. Available: https://www.edx.org/course/data-analytics-
learning-utarlingtonx-link5-10x. [Accessed: 13-Oct-2016].
[2] R. Baker, “Big Data in Education,” edX, 22-May-2015. [Online]. Available:
https://www.edx.org/course/big-data-education-teacherscollegex-bde1x.
[Accessed: 13-Oct-2016].
[3] Teachers College, Columbia University, “The Masters of Science Degree in Learning
Analytics,” Teachers College, Columbia University, 2015. [Online]. Available:
https://www.tc.columbia.edu/human-development/learning-analytics/. [Accessed:
13-Oct-2016].
[4] The Moray House School of Education, The University of Edinburgh, “Master of
Science in Digital Education,” The University of Edinburgh, 2015. [Online]. Available:
http://digital.education.ed.ac.uk/. [Accessed: 13-Oct-2016].
[5] T. McKay, “Practical Learning Analytics,” edX, 26-Aug-2016. [Online]. Available:
https://www.edx.org/course/practical-learning-analytics-michiganx-plax-0.
[Accessed: 13-Oct-2016].
[6] T. Rogers, “Learning Analytics and Digital Learning,” University of South Australia,
2016. [Online]. Available:
http://programs.unisa.edu.au/public/pcms/course.aspx?pageid=161021. [Accessed:
13-Oct-2016].
[7] Society for Learning Analytics Research, “LASI – Learning Analytics Summer
Institute,” LASI - Learning Analytics Summer Institute, 2016. [Online]. Available:
http://lasi.solaresearch.org/. [Accessed: 13-Oct-2016].
[8] LACE Project, “Learning Analytics Community Exchange,” LACE - Learning Analytics
Community Exchange, 2016. [Online]. Available: http://www.laceproject.eu/lace/.
[Accessed: 14-Oct-2016].