As soon as the conversation about learning data goes beyond “how many have completed the course?”, the abbreviation LRS turns up. It is often described as something large and complex, but the idea itself is simple. An LRS is the place learning data is sent to and where it stays. The question is rather whether you need one of your own, and that depends on what your learning actually looks like.
What an LRS is
LRS stands for Learning Record Store and is a data store specialised in learning data. It receives xAPI statements – short descriptions of what someone has done in the form actor, verb and object – stores them and hands them out again on request.
What makes an LRS more than an ordinary database is that the way of submitting and retrieving data is standardised. Any course, app or simulator can send in statements the same way, and any analytics tool can retrieve them the same way. That is the whole point: learning data from many sources is gathered in a format you can work with further.
What an LRS is not
This is where most of the misunderstandings arise, so it is worth being clear.
- It is not a learning platform. An LRS has no course catalogue, no invitations, no reminders and no participant administration. It does not deliver training; it receives data about training.
- It is not a reporting tool. Most LRS products have simpler views for looking at incoming data, but the real analyses are normally built in a BI tool or a custom reporting solution that pulls data from the store.
- It is not a content library. Courses, films and documents stay where they are. It is only the traces of use that end up in your LRS.
An LRS is therefore a puzzle piece, not a complete solution. It becomes useful only together with content that sends good data and some form of analysis at the other end.
How it relates to the learning platform
There are three common setups, and they differ more in practice than on the drawing board.
Built-in LRS in the learning platform
Many modern learning platforms have an LRS built in. That is the simplest way in: no extra system to procure, no integration to build, and data lands automatically next to the other course information. The limitation is that the store is often optimised for the platform’s own courses, and that you may struggle to get the data out in full if you change vendor.
Standalone LRS alongside the learning platform
Here the store stands on its own and receives data from both the learning platform and other sources. It costs more to set up and maintain, but you own the data model and the history independently of which platform you happen to use right now. It is the most common solution when learning happens in several systems.
LRS as a hub between several systems
In larger organisations the LRS is used as a shared junction. The learning platform, internal business systems, simulators, apps and perhaps classroom training report in to the same place, and from there data moves on to analysis and follow-up. That gives the best overall picture and also requires the most work on structure and governance.
What you get that you do not get otherwise
The clearest value is that learning outside the learning platform becomes visible. A completed course, a simulation in another system, a lookup in a checklist out in the organisation and a segment in a classroom training can all be described the same way and read together. Without a shared store, each system becomes its own island with its own reporting.
A second value that is often overlooked: the history survives a platform change. If you change learning platform every five years, the follow-up that sat there normally disappears too. If the data sits in a standalone LRS it remains, and you can follow development over a longer period than a single vendor relationship.
Do you need one?
The honest answer is that many organisations manage perfectly well without one. A dedicated LRS is probably unnecessary if:
- all learning happens in one learning platform and the courses are built in SCORM
- the questions you need answers to are completion rate, results and reminders
- the platform’s standard reports already answer what the organisation is asking
An LRS starts to become justified when any of the following applies:
- learning happens in several systems and you need to see the whole picture in one place
- you want to measure behaviour and effect, not only completion, and need evidence over time
- the requirements for traceability are high, for example in compliance, and the documentation needs to be kept for a long time
- you want to connect learning data with business data such as deviations, support cases or quality outcomes
- you have changed or plan to change learning platform and want to keep the history
A simple way to test the question: write down the five questions you would like to be able to answer in a year’s time. If the learning platform can already answer them, you do not need an LRS. If the answers require data from several places, it is time to look closer.
What you need to decide before you acquire one
The data model
xAPI decides the form of the statements but not the content. Without shared agreements on which verbs are used and what they mean, you get a store full of data that cannot be compared. Decide a small set of verbs and activity types from the start and stick to it.
Personal data and retention
An xAPI statement almost always points out an identifiable person, which makes the contents of an LRS personal data. Decide the legal basis, inform employees about what is registered and set retention rules before you start collecting. Also think through where the line runs between follow-up of training and monitoring of individuals. It is a question of trust at least as much as of compliance, and it is considerably easier to handle in advance than afterwards.
Where the data sits and how you get it out
Check where the store is hosted and which terms apply. Also check that you can export the entire contents in a standard format. One of the main arguments for a standalone LRS is independence, and that argument collapses if the data cannot be moved.
Volume and maintenance
Detailed collection quickly produces large volumes of data, and someone needs to own the store over time: keep the data model current, quality-assure incoming statements and make sure the reports continue to match. An LRS without an owner becomes an expensive archive.
Start small
The most common mistake is to build a comprehensive solution before anyone has used the data in a live setting. Instead choose a training programme where follow-up actually matters, formulate a few questions you want to be able to answer, measure only what is required to answer them and let the result decide the next step. Then you notice early whether an LRS solves a real problem for you, or whether the learning platform you already have is good enough.
We help you decide whether an LRS is needed, set a data model that holds over time and build training that sends data worth keeping.