Dr. Jaclyn Lee, PhD and IHRP-MP //
Articles
Learning Analytics, Its Importance, & Simple Steps To Implement It
By Dr. Jaclyn Lee, PhD and IHRP-MPPublished on May 11, 2016
It is actually the
measurement, collection, analysis and reporting of data
about learners for purposes of understanding and
optimizing their learning as well as tracking the
effectiveness of such programs. Why is it so important
to understand and grasp Learning Analytics? This is
because learning analytics helps to improve learning in the
following way:
One of the most significant benefits of analytics is that
they can provide insight into not only how a learner
is performing today, but also about his her future
performance throughout the duration of the course.
For example, online facilitators may foresee if a particular
learner is likely not to pass the eLearning course, or
if the learner is likely to pass the eLearning course if
additional support is provided (such as further readings or
tutoring sessions).
Through learning analytics, learning professionals and
online instructors gain the ability to custom tailor learning
experiences for each and every individual learner.
For example, learners can be provided with links to sites
that may help them to effectively comprehend the topic, or
videos that allow them to learn through a more auditory/
visual approach.
Given that more learners have the opportunity to enhance
their performance thanks to learning analytics data and
intervention; fewer learners will drop out or fail the course.
For example, if a learner isn’t faring well throughout
the eLearning course, then he/she is less likely to be motivated to remain enrolled. As a result, a learner will
simply stop participating.
Not only can learning analytics help current learners, but
can also help future learners as well.
For instance, if the data shows that a vast majority of
learners are finding one particular aspect of the eLearning
course too challenging, then the developers can change
the difficulty level of that specific eLearning module.
This will lead to more powerful and impactful eLearning
environments tomorrow, thanks to the data that has been
collected today.
For example, if you determine, through analytics, that a
particular section of the eLearning course simply isn’t
helping learners to achieve their learning goals, then you
can devote your resources to either improving it or focus
on another area that may be a more worthwhile investment.
In an economy where resources are particularly constrained,
L&D executives are under more pressure than ever to
demonstrate their value and answer some pointed questions
from their peers such as:
- Which training need is likely to yield the best return on investment?
- When should I use each delivery media?
- Which media works best with specific workforce segments (e.g. new hires, sales force, senior leaders)?
So How Do You Implement Learning Analytics?
To Begin With, Start With Some Simple Steps On
Your Learning Analytics Journey
1. You can invest in solution or software that has the capabilities to analyze data using algorithms to predict learners behavior, mainly for e-learning platforms. An example is the Moodle Learning Management System. You can find out more here
2. If you don’t have a sophisticated software package, you can use Kirkpatrick’s model, level 2 or level 3 post course evaluation. Tracking before and after training performance will help you to understand learning patterns and behaviors.
Level 2 - Degree participants acquire the intended knowledge, skills and attitudes based on their participation
Level 3 - Degree participants apply what they learn on the job
Level 4 - Targeted outcomes occur after learning
3. Looking at the trends of attendance sheets in training programs will also help you to understand the demands for certain learning programs.
4. Linking Training Strategy to the Business strategy so that L&D can show a direct impact to the bottom line. Training Scorecards can be developed to link individual programs to the business needs of the organization. From there, you can use analytics to track individual learners to see if the programs they are attending is impacting the organization. Learning measurements can come in the form of job effectiveness, job impact and business results. Other examples can include average change in performance appraisal ratings over time, customer satisfaction ratings, employee engagement scores, turnover and productivity.
5. Lastly, always remember that when you use learning analytics to draw results, it is always important to use anecdotal information to confirm the numbers and to highlight data with meaningful examples.
Good luck in your journey of implementing LEARNING ANALYTICS in your organization.
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