“Measure, measure, measure,” is the motto of the modern digital business. Data-driven analytics have improved business processes that would otherwise be invisible to many industries. With the rise of distance learning across the world, these same analytics models are being used to make the learning process more effective.

Online learning efficiency has been a major debate in the education systems over the last year. Pandemic restrictions may have prevented an estimated 463 million learners from getting access to education, due to lack of infrastructure or learning devices. In the enterprise, reskilling the workforce will be a major trend, as 3 in 4 business leaders plan to continue remote training programs, but worry that they may not be as efficient as in-person experiences.

“What Gets Measured, Gets Managed”—Peter Drucker

The online business world has been deploying analytics as a core driver in digital transformation. Organizations use these analytics for data-driven decision making, which helps reveal insights on how the company can become more efficient, or how certain processes can be improved through automation.

It is no surprise then, that data-driven eLearning providers are ‘borrowing’ this analytical strategy as part of their learning software, enabling them to understand the intricacies of student progress. When an educator is able to measure a learner’s results, they can explore more effective modes of instruction, such as increasing challenges for top-performers, or shoring up weaknesses for those with gaps in learning. When course-planners can understand how certain results are being achieved, they have better ways to make proactive decisions or changes.

Learning Management Software (LMS) can take these analytical strategies to a granular level, focusing on each individual learner’s needs. Learning analytics can support automated decision-making, allowing real-time coursework corrections as the learner makes progress. For example, Polcode’s eLearning development services have enabled our LMS clients to implement AI and analytics at scale, so that when each student logs in, they are given coursework that is personalized to their current progression. On the administrator’s side, we’ve also developed reporting features that can shed light on how students are making progress through simple data visualizations that can be configured in a single dashboard. 

Business analytics help organizations reduce risks. By helping them make the right decisions based on available data such as customer preferences, trends, and so on, it can help businesses curtail short and long term risks.

There is no denying it that business analytics have come to change the dynamics of businesses and how they operate. Its importance cannot be overestimated, and with more and more companies relying on it for their decision-making process, it is something your business should consider incorporating if it hasn’t been done so already.

“If you do not know how to ask the right question, you discover nothing”—W. Edward Deming

If you’ve never worked with AI or Analytics before, there’s one key thing to know: large volumes of data are necessary for the models to work, and asking the right questions is paramount to create useful AI analytics models. Luckily, most eLearning platforms passively generate tons of useful data that can be leveraged via reporting metrics and analytics tools. 

Making sense of your learning data means asking the right questions. So here are just a few of the top metrics to map using eLearning analytics that can help your business improve educational efficiency.

Course Metrics

• Course health. How many courses are in progress, and how many have been completed? Knowing the most popular courses by category helps eLearning providers know which direction to invest their resources.
• Enrollment. What kind of students are currently enrolled, and how many of them unenrolled? Seeing real-time enrollment data or graphing enrollment over time, can reveal previously hidden insights or trends in participation.
• Viewership. How many students have viewed the course, but haven’t enrolled? The ecommerce industry uses similar metrics to identify shopping behavior, and adapt their product portfolios to promote (or even remove) content which isn’t working out.
• Friction. At which points do students struggle or drop a course? Is there something in the software, courseware, or course path that needs adjustments? When do they drop out from watching a video? Identifying problem spots in a learner’s journey can shed light on issues with the platform itself, or the content being presented.
• Segmentation: Which types of users find this course most helpful, or which types of users struggle with this particular course? Understanding your users better means that you can offer them more targeted content that they’re likely to consume.

Time-spent Performance

• Course time. How long on average do students spend on a particular course? Having accurate estimates of time-spent can lead to better engagement, as students and educators get better insights into time commitments.
• Last login. When was the last time the user logged in? If they haven’t logged in, your automation services can know when it’s time to send a reminder.
• Current activity. Which users are currently learning in real-time? Knowing when users are taking courses can be helpful for planning ideal time frames down the road.
• Overall activity. Tracking user activity metrics, like number of comments, how many hours viewed of video, etc. offers the big picture of engagement on your platform.

Assessment Analytics

• Scores. See all scores at-a-glance, with aggregated analytics that look at the overall results of a program, course or specific test. 
• Completion time. Know the average time spent taking a test or quiz, so that you can offer accurate time estimations before other users take the same exam.
• Answer distribution. Analytics makes it possible to find trends in commonly missed questions, showing the scale of difficulty for each problem across many learner groups. Additionally, getting a breakdown of each question offers previously hidden insights, letting educators know if their questions are hitting the mark.
• Learner segments. See which users are performing at a high level after assessment, and which users need more help. eLearning platforms can take a segmented approach to each group, challenging the high-performers with harder bonus content, and help low-scoring performers fill in the gaps.

Gamification Metrics

• Badges. Measure the success of your gamification features and determine how ‘worth it’ gamifying techniques are in relation to each part of a learner’s journey.
• Engagement. Determine what kind of engagement activities are most-liked by learning groups, and how to improve engagement in spaces where activity is needed most.
• Competition. Measure the interaction levels between students and how they fare competitively in rank-based systems.

“One Pace Does Not Fit All. And That’s OK”—David Levin

There is one noble goal of having an analytical approach to eLearning software development: Personalized learning helps every student make progress, no matter where they are on their journey. As a byproduct, educators and administrators gain the ability to make informed decisions, creating clear pathways for educational or business goals.

Launch Personalized Learning For Your Own Platform

Personalized learning is easy to implement with the right development partner. With just a few AI Analytics modules, eLearning software developers can reduce the time it takes for your platform to make sense of data, increasing engagement, knowledge retention and motivation. 

At Polcode, we help educational organizations of all sizes add Analytics and AI to their learning experiences, with fast turnarounds and expert consultation from our EdTech developers. Check out our eLearning software development services, if you’d like to learn more!