Defining Learning Analytics Learning Analytics Megan ...doc/71-… · to Learning Analytics...
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S 1 0 0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 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 expectations For student success: course completion college retention, content mastery and learning outcomes s Reduce costs of education Provide early interventions Improve how education is delivered Defining Learning Analytics 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 0 0 0 1 1 0 1 1 1 0 0 1 0 0 0 1 1 1 1 1 Capture Report Act Data Sources Interventions Student information systems LMS Extracurricular activities Clickers Surveys Set policies Require visit to support services Make referrals Send notifications Connect to curated 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 Michigan Student Explorer Early warning system for 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 connection between library data and wider data initiatives. Requires an organizational culture that understands & values data- informed decision making. 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 Information Technology Unit Dedicated Analytics Center Chief Academic Officer Chief Information Officer Chief Business Officer Student Success Lead IT and I nstitutional 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 back to institutions so that they can rematch the data with unique student identifiers. Student success maturity Support Funding Policies Collaboration/participation Data support Decision-making framework Analytics maturity Data-driven culture Commitment Staffing/resources Infrastructure Data efficacy Successful learning 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, a nd 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 successful learning 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 To 0 0 1 1 1 1 0 0 0 1 1 Faculty Students Advisors Administrators Brightspace Student Success System Reports show risk patterns; “Success Index” shows a student’s predicted final grade
Transcript of Defining Learning Analytics Learning Analytics Megan ...doc/71-… · to Learning Analytics...
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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
