An LRS (Learning Record Store) aggregates, analyzes, and automates learning data from various sources to provide insights and improve training. It centralizes messy learning data into a single, standardized format for deeper analysis of training effectiveness and impact on business metrics. Automated processes can then use the centralized LRS data for personalization, recommendations, adaptive learning, and A/B testing to enhance the learning experience.

1. What is an LRS?
Ian Blackburn
Learning Locker - Product Manager

2. What will an LRS do?
Aggregation Analysis Automation

3. What will an LRS do?
Aggregation Analysis Automation

4. Most companies data is messy...

5. xAPI can standardise it
Learning Locker makes it easy to
measure hard things. All your data in
one place in one format.
Connecting external platforms to
take a deeper dive or automate
becomes much easier now that you
have a centralised location for your
data.

6. What will an LRS do?
Aggregation Analysis Automation

7. Analysis
One of the main aspects of L&D is the ability to use it to
tell us how effective our training has been.
Using data from both ‘participation’ sources (who did
what) as well as ‘performance’ sources (how did their
behavior change) allows us to not only measure the level
of success after the fact, but also use the data to make
predictions and demonstrate how training can have a
positive impact on other KPI’s.

9. Analysis

11. Measuring the impact
of training against
business KPIs

15. What will an LRS do?
Aggregation Analysis Automation

16. Automation
Data can be used for more than just analysis. Ideally, data
is automatically being used throughout the learning
ecosystem for recommendations, personalization,
adaptive learning, nudges and even A/B testing of learning
campaigns.
All of this becomes possible when the data is centrally
located, easy to access and easy to extract. For example,
the Sparks App uses this data to create branching, smart
paths based on triggers throughout the learning
ecosystem.

19. Trigger

20. Action

21. Validation

22. Thank you