This document outlines a 5-step process model for learning analytics that includes analysis and design, development and implementation, acquiring data, reporting and analysis, and adapting to data. Each step produces artifacts to document decisions and justify the process. Following this model can help implement, grow, and sustain an effective learning analytics program.

1. A Viable Process Model For Learning Analytics
5th Logic, Inc
459 Herndon Parkway, Suite 8
Herndon, VA 20170
nwashburn@5thLogic.com
©2018 5th Logic. All Rights Reserved
Nick Washburn
Chief Product Officer

2. October 23, 2018
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A Process Model for better “Evaluation”
• Drills down into the “E” of ADDIE – Evaluation
• Each step in a process model produces artifacts – why are
artifacts important?
• Justification! - documented reasons for decisions made
• Helps to maintain, sustain, prove impact, and more
• You can follow this model to implement, grow, and sustain a
vibrant learning analytics program
• This is highly technical stuff if you hire a consultant demand
artifacts, processes, exit strategy, success criteria.
• Process works for you – you don’t work for the process
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Goal. Get to a planned ecosystem.
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Image Source: https://baldfatguyandholyrollerblog.files.wordpress.com/2016/07/aaa1.jpg?w=1024&h=1084
Is your learning ecosystem planned?

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A Process Model For Learning Analytics
1. Analysis & Design – Understand current system and “to be system”, prototype,
focus groups, Design Architecture, Proof of Concept, more
2. Develop & Implement – Follow the plan, iterate.
Build, Data connectors, set up databases, LRS, Implement strategies for collecting
digital learning and performance activity data – xAPI reporting to anything digital
3. Acquire the Data – Database(s) Data Stores, collects the contextual data, LRS, other
4. Report & Analyze – Dashboards, Visualizations, Leaderboards, and create reports on
the data summative and formative evaluation
5. Adapt to the Data – React - Create rules, take action, automate, adapt, predictive
analysis, triggers and rules, big data stuff
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Trajectory and goals will determine artifacts.

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A Process Model For Learning Analytics
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Yes, I went there.
A/D D/I 3 4 5
A D D I E

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1. Analysis & Design
POTENTIAL ARTIFACTS
• Current system designs and processes
• Design to be system designs and
processes
• Learning Objectives
• Profiles – xAPI, Other
• Prototype or Proof of Principle
• Research documentation, Case Studies,
White papers, etc.
• Set up source control
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Understand current system and design “to be system”, analysis
and planning, profiles, etc.
• Develop a Sprint Plan, Develop a
schedule/budget
• Set up change management inquiry
within the organization
• Project Management Plan
• Set Goals and Clear Success Criteria –
“What does done mean?”
• Decision Analysis & Resolution (DAR)
• Estimating Methodologies
• Focus Groups, More?

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1. Analysis & Design
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2. Develop & Implement (Deploy)
POTENTIAL ARTIFACTS
• Data connectors,
• set up databases, LRS, BI
• Implement strategies for collecting
digital learning and performance
activity data
• Add xAPI reporting to anything digital
• Design new content with xAPI
reporting
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Follow the development plan, Iterate.
• Source Code
• Follow Sprint Plan
• xAPI Profiles
• Track deficiencies
• Set up models

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3. Acquire the Data
POTENTIAL ARTIFACTS
• A Conformant LRS or multiple
• Data bases in organization
• Share data with other departments for
their reporting
• Systems integration work to connect
data sources
• Artifacts Big Data Tools (e.g., Mongo
DB, Google Cloud Platform,
Elasticsearch)
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Database(s) Data Stores, collects contextual activity stream data,
LRS, Performance data, HRIS, other
• Collect Deficiency data
• More?

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4. Report & Analyze
POTENTIAL ARTIFACTS
• What views or correlations of the data
are needed to help reach goals?
• Learner audience views
• Educator/Instructor views
• C-Level Stakeholder views
• Artifacts Big Data Tools (e.g., Mongo
DB, Google Cloud Platform,
Elasticsearch)
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Dashboards, Visualizations, Leaderboards, and create reports
on the data summative and formative evaluation
• More?

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5. Adapt to the Data
POTENTIAL ARTIFACTS
• Manually change the content according
to findings in step 4
• Automate tutoring
• Recommendations engine
• Use performance data to trigger
training needs
• Artifacts Big Data Tools (e.g., Mongo
DB, Google Cloud Platform,
Elasticsearch)
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React - Create rules, take action, automate, adapt, predictive
analysis, triggers and rules
• More?

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Thank you
Published materials
Learning Solutions Magazine
https://www.learningsolutionsmag.com/authors/
594/nick-washburn
Association For Training and Development (ATD)
https://www.td.org/Publications/Author.aspx?Ite
mId=6BC586A46E8440BE969656516A50E7EF
Publications
https://www.linkedin.com/in/nick-washburn-
1b51177
ADL including downloadable research paper
https://www.adlnet.gov/reaper/
©2018 5th Logic. All Rights Reserved
Nick Washburn
Chief Product Officer
5th Logic, Inc
459 Herndon Parkway, Suite 8
Herndon, VA 20170
nwashburn@5thLogic.com
IEEE LTSCI TAG Base xAPI Standard Work Group
https://www.tagxapi.org/

14. www.5thLogic.com
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