Going Big: University of Wisconsin’s Move Towards Systemic Learning

Analytics

Linda Jorn Assoc. Vice Provost of Learning Technologies & Director of Academic Technology

Kimberly Arnold Researcher

Bruce MassCIO &Vice Provost for IT

#LAS2014

Agenda•What we know about learning analytics

•Getting real about learning analytics

•Frameworks and tools to help us

•Big ideas that will move us forward •Open Learning Analytics

• Educational Innovation

#LAS2014

What We Know about Learning Analytics?

•Sea of data meaningful information and action

•LA efficacy research shows us that • Student engagement can be increased• Creating more supportive learning environments• Improving learning outcomes• Providing deeper formative assessment for teachers and learners• Increasing retention and graduation

#LAS2014

What We Know about Learning Analytics?

Paradigm shift of what learning means in knowledge economy

• Student empowerment will be essential in the context of personalized learning • Students need to be stewards of their own education

#LAS2014

Getting Real about Learning Analytics •LA can be truly transformational for education

•LA research is vital; its rigor must inform practice

• We need to go BIG; scale and sustain

#LAS2014

Towards a Learning Analytics System

Image by LegacyDad

#LAS2014

Image by Roy Dabner

#LAS2014

Big Ideas that Will Help Us Move Forward

Open Learning Analytics

Educational Innovation

#LAS2014

First, Some Context

Disruption of Higher EdScale, Scale, Scale

UrgencyUnizin (www.unizin.org)

#LAS2014

Big ideas that Will Help Us Move Forward

Open Learning Analytics

#LAS2014

“Openness of process, algorithms, and technologies is important for innovation and meeting the varying contexts of implementation.”

“Modularized integration of core analytic tools is crucial.”

“Reduction of inevitable fragmentation by providing an integrated, expandable, open technology that researchers and content producers can use in data mining, analytics, and adaptive content development.”

~Society of learning Analytics Research

#LAS2014

Open Framework for Learning Analytics

Open Learning Analytics Community created a framework:• Data Capture, Storage and Security Standards Framework

• Analytics Framework

• Intervention Framework

• Adaptive Content Framework

• Privacy and Ethics Framework

#LAS2014

OLA Summit Major Takeaways

•Openness must be a top priority

• Time to bring disparate communities together

• “Open” doesn’t mean “free”

•We need to productively engage vendors

•We need to start focusing on data that matters not what we have

#LAS2014

OLA Summit Outcomes

Working groups around domains•Open standards/APIs/software

•Open research (data, models)

• Institutional strategy and policy issues

• Learning science/Learning design

#LAS2014

Big Ideas that Will help Us Move Forward

Educational Innovation

Educational Innovation at UW-Madison

“We must find ways to stimulate and scale change across institutions - as well as to sustain those changes - if we are to create models that can serve the expanding needs of our learners.”

~David Ward

#LAS2014

How Can We Sustain Strategic Innovation and Transitions in Higher Education?

July/August 2013

EDUCAUSE review

#LAS2014

Year Three of EI (2013-2014)

Sustaining and Scaling

• Quality learning at a world-class

public research institution

• Access to UW Madison Wisconsin

learning experiences across

Wisconsin

• Global Learning Experiences

Year Three of EI (2013-2014)

Experimenting and scaling best practices

• Transformative Practice Design

Teams

•Mobile Learning

• Learning Analytics

• Self-paced modularized learning

#LAS2014

Year Three of EI (2013-2014)

Experimenting and scaling best practices• Learning analytics, as contextually

defined for UW-Madison, is the

application of analytic techniques to a

data set(s) with the end goal of

providing insight and action to evaluate

and/or improve teaching and/or

learning; ultimately, to support the

overall educational mission of the

institution.

#LAS2014

Two-Pronged Approach

Advancing State of the Art Learning Analytics

Exploration, Pilots,

Collaboration, and Outcomes

Advancing Foundational

Needs

Governance, Policies, and Standards

Data Quality

Technical Infrastructure

EI Learning Analytics Design Team Suggested Roadmap

#LAS2014

Advancing State of the Art Learning Analytics

•Pilots•Early warning systems—predictive analytics•Student utility belt – “Quantified-self”

#LAS2014

Data Governance and ethical Guidance Framework for LA

Guiding Policies

Clear Standards

Moral, Ethical,

and Practical Issues

Data Collectio

n, Storage, Transfer, Analysis,

and Deletion

#LAS2014

Investment in Data Quality

“You can’t be analytical without data, and you can’t be really good at analytics without really good data.”

~Thomas Davenport

#LAS2014

Data Quality Audit

•Pipino, Lee & Wang’s data risk types

•Accessibility•Believability•Completeness

•Consistency• Interoperability•Relevancy

#LAS2014

Expected Outcomes of a Data Quality Audit• Identified data stewards with appointed roles

for the collection and curation of data elements needed

• Clear, well-communicated data definitions and quality standards for all data

• Plans for the collection and curation of new data elements necessary

• A campus-wide data dictionary

#LAS2014

Investment in LA technical Infrastructure

#LAS2014

User Interface Users

Governance Who gets to see what

Learning Analytics Data Information

Universal Data Format / Quality

Data sources – CMS, Registrar, etc.

Pilots

Investment in LA Technical Infrastructure

#LAS2014

#LAS2014

Discussion