ACRL e-Learning webcast series: Learning Analytics – Strategies for Optimizing Student Data on Your Campus

This three-part webinar series, co-sponsored by the ACRL Value of Academic Libraries Committee, the Student Learning and Information Committee, and the ACRL Instruction Section, will explore the advantages and opportunities of learning analytics as a tool which uses student data to demonstrate library impact and to identify learning weaknesses. How can librarians initiate learning analytics initiatives on their campuses and contribute to existing collaborations? The first webinar will provide an introduction to learning analytics and an overview of important issues. The second will focus on privacy issues and other ethical considerations as well as responsible practice, and the third will include a panel of librarians who are successfully using learning analytics on their campuses.

Webcast One: Learning Analytics and the Academic Library: The State of the Art and the Art of Connecting the Library with Campus Initiatives
March 29, 2016

Learning analytics are used nationwide to augment student success initiatives as well as bolster other institutional priorities.  As a key aspect of educational reform and institutional improvement, learning analytics are essential to defining the value of higher education, and academic librarians can be both of great service to and well served by institutional learning analytics teams.  In addition, librarians who seek to demonstrate, articulate, and grow the value of academic libraries should become more aware of how they can dovetail their efforts with institutional learning analytics projects.  However, all too often, academic librarians are not asked to be part of initial learning analytics teams on their campuses, despite the benefits of library inclusion in these efforts.  Librarians can counteract this trend by being conversant in learning analytics goals, advantages/disadvantages, and challenges as well as aware of existing examples of library successes in learning analytics projects.

Learn about the state of the art in learning analytics in higher education with an emphasis on 1) current models, 2) best practices, 3) ethics, privacy, and other difficult issues.  The webcast will also focus on current academic library projects and successes in gaining access to and inclusion in learning analytics initiatives on their campus.  Benefit from the inclusion of a “short list” of must-read resources as well as a clearly defined list of ways in which librarians can leverage their skills to be both contributing members of learning analytics teams, suitable for use in advocating on their campuses.

my notes:

open academic analytics initiative
https://confluence.sakaiproject.org/pages/viewpage.action?pageId=75671025

where data comes from:

• students information systems (SIS)
• LMS
• Publishers
• Clickers
• Video streaming and web conferencing
• Surveys
• Co-curricular and extra-curricular involvement

D2L degree compass
Predictive Analytics Reportitng PAR – was open, but just bought by Hobsons (https://www.hobsons.com/)

Learning Analytics

IMS Caliper Enabled Services. the way to connect the library in the campus analytics https://www.imsglobal.org/activity/caliperram

student’s opinion of this process
benefits: self-assessment, personal learning, empwerment
analytics and data privacy – students are OK with harvesting the data (only 6% not happy)
8 in 10 are interested in personal dashboard, which will help them perform
Big Mother vs Big Brother: creepy vs helpful. tracking classes, helpful, out of class (where on campus, social media etc) is creepy. 87% see that having access to their data is positive

librarians:
recognize metrics, assessment, analytics, data. visualization, data literacy, data science, interpretation

INSTRUCTION DEPARTMENT – N.B.

determine who is the key leader: director of institutional research, president, CIO

who does analyics services: institutional research, information technology, dedicated center

analytic maturity: data drivin, decision making culture; senior leadership commitment,; policy supporting (data ollection, accsess, use): data efficacy; investment and resourcefs; staffing; technical infrastrcture; information technology interaction

student success maturity: senior leader commited; fudning of student success efforts; mechanism for making student success decisions; interdepart collaboration; undrestanding of students success goals; advising and student support ability; policies; information systems

developing learning analytics strategy

understand institutional challenges; identify stakeholders; identify inhibitors/challenges; consider tools; scan the environment and see what other done; develop a plan; communicate the plan to stakeholders; start small and build

ways librarians can help
idenfify institu partners; be the partners; hone relevant learning analytics; participate in institutional analytics; identify questions and problems; access and work to improve institu culture; volunteer to be early adopters;

questions to ask: environmental scanning
do we have a learning analytics system? does our culture support? leaders present? stakeholders need to know?

questions to ask: Data

questions to ask: Library role

learning analytics & the academic library: the state of the art of connecting the library with campus initiatives

questions:

literature

• 7 Things You Should Know About First-Generation Learning Analytics. Published: Tuesday, December 6, 2011
  https://library.educause.edu/resources/2011/12/7-things-you-should-know-about-firstgeneration-learning-analytics
  
• Barneveld, A., Arnold, K., & Campbell, J. (2012). Analytics in Higher Education: Establishing a Common Language. Educause Learning Initiative. Retrieved from http://net.educause.edu/ir/library/pdf/ELI3026.pdf

  Brown, M. (2012). Learning Analytics: Moving from Concept to Practice – ELIB1203.pdf. Educause Learning Initiative. Retrieved from https://net.educause.edu/ir/library/pdf/ELIB1203.pdf

  http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education

  http://guides.lib.umich.edu/learninganalytics

causation versus correlation studies. speakers claims that it is difficult to establish causation argument. institutions try to predict as accurately as possible via correlation, versus “if you do that it will happen what.”

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More on analytics in this blog:

https://blog.stcloudstate.edu/ims/?s=analytics&submit=Search