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Statistical evaluation of rich
data sources to discern patterns that
can help individuals at companies,
educational institutions, or
governments make more informed
decisions. – EDUCAUSE (2011)Institutional Priorities
for Learning Analytics
Student Reactions
to Learning Analytics
Students support institutional collection of data on them about their progress toward a degree or certificate.
77%
Good
idea!
6%
Bad
idea
17%
Neither
good
nor bad
Provide learners with insight into their own
learning habits
Respond to expectationsFor student success: course completion
college retention, content mastery and learning outcomes
sReduce costs of education
Provide
early interventions
Improve how education
is delivered
Defining Learning Analytics
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CaptureReport
Act
Data Sources InterventionsStudent information systems LMS
Extracurricularactivities
Clickers
Surveys
Set policies
Require visit to support services
Make referralsSend notifications
Connect tocurated interventions
Descriptive
Describes what is happening
Includes progress “traffic lights”
Generates personal messages
Purdue University Signals
Diagnostic
Determines obstacles & facilitators of student success
University of MichiganStudent Explorer
Early warning systemfor advisors
Identifies at-risk students
Predictive
Uses data to predict likely student success or failure Learning analytics
projects are an opportunity to extend the means by which libraries can show their contributions to institutional student learning and success goals.
Lack of connectionbetween library data and wider data initiatives.
Requires an organizational culture that understands & values data-informed decisionmaking.
Opportunities Challenges Threats
Flagging at-risk students may be self-fulfilling.
Proprietary algorithms may result in misleading patterns or misclassifications.
Getting Connected
Stakeholders to Know
Technology to Watch
Climate Considerations
Institutional Research
InformationTechnologyUnit
Dedicated Analytics Center
Chief Academic Officer
Chief Information OfficerChief Business Officer
Student Success LeadIT and Institutional Research Partnerships
Caliper: Interoperability standard enabling data connections within the Next Generation Digital Learning Environment (NGDLE).
Predictive Analytics Reporting (PAR) Framework (Hobsons): Uses de-identified student data provided by multiple institutions to predict student success, then returns the prediction data backto institutions so that they can rematch the data with unique student identifiers.
Student success maturity
Support
Funding
Policies
Collaboration/participation
Data support
Decision-makingframework
Analytics maturityData-drivenculture
Commitment
Staffing/resourcesInfrastructure
Data efficacy
Successfullearning analytics initiatives require
librarian skills including data analysis, interpretation,visualization, understanding
of relationships between educational data, instructional
design and planning for interventions.
Silo Busting Skills
Data skills
Instructional skills
Librarians can leverage these skills to connect to--and
enrich--learning analytics projects at their institutions.
Libraries contribute skills in data management, analysis, visualization, and interpretation.
Libraries contribute skills in intervention planning, instructional design, outcomes assessment.
Librarians are skilled at connecting across academic and administrative units. Building such bridges is key to a successfullearning analytics ecosystem.
Learning Analytics
& Libraries:
A Primer
Megan Oakleaf, MLS, Ph.D.Syracuse University
Meg Grotti, MLIS, M.Ed.University of Delaware
Samantha Settimio, M.A.Syracuse University
To0
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FacultyStudentsAdvisorsAdministrators
BrightspaceStudent Success System
Reports show risk patterns;“Success Index” shows a student’s predicted final grade
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