Learning Analytics, Its Importance, & Simple Steps To Implement It

What is Learning Analytics exactly?
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:

1. Helps To Predict Learners’ Performance
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).

2. Provide Learners With A Personalized Learning Experience
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.

3. Increase Their Retention Rates
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.

4. Helps To Improve Future Course Planning
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.

5. Boost Cost Efficiencies
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)?

Again, Learning Analytics helps answer these questions by applying statistical methods to find the hidden correlations in the HRIS data that you already have. You can draw up very interesting patterns and valuable information from the Analytics platform that can greatly facilitate your decision making.

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 1 – Reaction
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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Dr. Jaclyn Lee

Dr. Jaclyn Lee, PhD and IHRP-MP