LTAC
Project Group Under Formation:
Information Analytics – Student Learning Data
The
work of
the Learning Technology Advisory
Council (LTAC) Information Analytics (IA)
project will develop higher education IA use cases
and create
prototypes and demonstrations to show the results in practice. The
resultant IA
specifications have strong
potential for applying existing IMS technical standards (e.g., Enterprise
Services, Question
and Test
Interoperability [QTI], Reusable
Definition of
Competency or Educational Objective [RDCEO], and ePortfolio)
for the
gathering, compiling, and analysis of student learning data.
Additionally,
the intent of this group's work product will enable higher education IT
professionals and academics (and third parties that support them) to
leverage a
discrete set of methods and technologies to facilitate the use of
learning data
to promote student success - while allowing those institutions to
accommodate
their unique legislative, normative, and student-driven standards of
privacy.
Participants
IMS
Information
Analytics Call for Participation
The
Current Challenge
The
convergence of external accountability pressures (such as the Spellings
Commission and Bologna
Accord), academic systems collecting increasing amounts of
data about
student effort and learning, and the adoption of data warehousing and
business
intelligence technologies is providing higher education with
unprecedented
options for making decisions based on analytics. At the same time,
institutions, industry associations, and regulators are presenting
college
performance information in new venues on a near-daily basis. For
example, in the United
States,
institution-specific and consortial approaches to addressing the call
for
accountability include but are not limited to: Purdue
University, Baylor
University, Sinclair
Community College, University
of Alabama, Presidents’ Forum on Transparency by
Design, NASULGC/AASCU:
College Portrait, Association
of American Universities, and U-CAN:
The NAICU Consumer Information Template.
While
current analytics efforts are at the surface of telling higher
education what
factors lead most reliably to student success, experimentation is
underway
across academia to identify those factors, as identified above.
However, even
such limited experimentation raises serious concerns about student
privacy.
These concerns limit faculty and researchers' access to data or place
projects
on hold until proper mechanisms are developed for storing and sharing
analytical results.
The
Proposed Solution
Leveraging
the IMS GLC Contributing
Members, Executive
Strategic Council
and Learning
Technology Advisory
Council (LTAC) as well as invited industry experts, the IA
PUFSIG is
currently identifying and prioritizing the needs for IA use cases aimed
at
collecting and analyzing student learning and success data - predictive
and
assessment. Use
cases will be collected
or developed from interviews with higher education executives
(presidents,
provosts, etc.) as well a department heads, faculty, and information
technology
personnel to provide a holistic approach to developing IA use cases and
addressing the challenges discussed above. The IA PUFSIG will also
solicit
input and use cases from the individual
institutions and consortiums listed above to help with streamlining use
case
development and ensuring real-world applicability.
Subsequently,
the IA project group will work
with various IMS technical specification development groups to develop
the
technical standards to integrate existing IMS technical standards and
learning
enterprise systems to capture and analyze student learning data. The
resultant IA
standard(s) will enable
individual and aggregate predictive modeling and assessment reporting
for
student learning and success; and, define the architecture of privacy
surrounding the capture, analysis and reporting of these data.
Current
Activities
The
IMS IA PUFSIG is currently identifying and
prioritizing the IA use case needs for higher education based on active
work in
this area (see above) and priorities defined by higher education
executives,
department heads, faculty, and regulators. Upon prioritizing IA use
case
needs, the IA PUFSIG will begin developing
a formal IMS Project Charter defining the scope of work, deliverables
and
timeline associated with this project.
Related
Information
For related
information, please visit:
Further
Information
For
information on the Information Analytics
project group under formation, please contact:
John Falchi
Chief Program Strategist
IMS Global Learning Consortium
email: jfalchi@imsglobal.org
office: 919.656.0343
For information on IMS GLC and the
benefits of being a Contributing
Member, please
contact:
Rob Abel
Chief Executive Officer
IMS Global Learning Consortium
rabel@imsglobal.org
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