Globally, organizations spend millions of dollars on
training. However, the million dollar question is if the training was
effective. This is accompanied by two equally important questions—did it create
the required impact the business was seeking, and did it create the Return on
Investment (ROI) to justify the training spend?
While we agree on the need to secure answers to all these
questions, the reality is that this task is tough. It is in this context that
the usage of Learning Analytics comes to your rescue. You can
use the Learning Analytics to:
- Validate
if the learner interaction with the training was as planned.
- Assess
the effectiveness
of the training and its impact.
- Get the
pointers necessary to see if positive ROI is being generated.
In this blog, I outline why investing in Learning Analytics
makes business sense.
Most of us may be familiar with the Kirkpatrick
Model of Training Evaluation as shown here. Created in 1959, the model
has undergone significant change to adapt to the current day scenario. See its
transition from a “pyramidal” approach to the current “chain of evidence”
approach.
However, both of them feature 4 levels of training
evaluation namely:
- Level
1: Reaction (Learner’s reaction to the training—if it was useful and
relevant)
- Level
2: Learning (Learning gain that can be attributed directly to the
training)
- Level
3: Behavior (Required change or gain that can be attributed directly to
the training)
- Level
4: Impact (Demonstrable impact on the business)
NOTE: Its variation with Level 5 featuring the
ROI determination is another commonly used approach (Kirkpatrick-Phillips
Evaluation Model of training).
The power of the model lies in its approach of:
- Identifying
what is being measured.
- Using
the outcome of the evaluation outcome
to update/enhance and improve the training and its impact.
As you will note, this model focuses on:
- Understanding
the learner’s behavior.
- What
should be done to:
- Improve
learner motivation.
- Create
deeper learner engagement.
- Facilitate
the application of learning on the job.
This is exactly where the usage of Learning Analytics
comes in.
- You
can use its insights to create better learner interaction, sticky
learning, and facilitate the application of acquired learning.
- Furthermore,
with the help of learning analytics, you can support the learner in
gaining the required proficiency through practice sessions and specific
feedback to improve this further.
Specifically, through Learning Analytics:
- You
can gain tremendous insights on the learner’s interaction through (SCORM
or the Tin Can API). You can use the analytics to compare the output
against the assumptions used to create the learning design.
- You
also have access to voluminous data or Big Data that is captured on
LMS/LCMS, Portals and Polls/Surveys/Assessments done by learners. By
analyzing this, you can gain insights on:
- How
learners interact with online
training and how do they learn.
- If
the required impact is being created (new skill gain or bridging a skill
gap).
- If
the business sees the required gain.
- All
these insights can be fed back to update the learning design look with
measures to provide remediation, reinforcement, and practice to master the
skill.
- More
significantly, we can also provide specific or personalized feedback to
the learner.
NOTE: It is anticipated that in the next few
years, Artificial Intelligence (AI) will be used to enhance the usage of
Learning Analytics and create highly personalized cues for learners.
As you would have noted usage of Learning Analytics can help
you in progressive improvement of your learning strategy (based on data and not
assumptions), eventually leading to the required impact (for both learners and
business) and higher ROI.
I hope this blog gives you the required pointers on why
investing in Learning Analytics makes business sense and how you can leverage
on it to create high impact training. If you have any queries, do contact me.
Schedule a call with our Solutions Architecting Team.
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