Customizing LMS Analytics: Essential and Optional Metrics for Every Scenario
Analytics in an LMS is powerful. To show how impactful it can be, let’s start with three real-world examples.
While the examples below aren’t recent or based on traditional LMS platforms alone, they were conducted by leading institutions and remain highly influential.
Each case highlights how analytics—when thoughtfully integrated into a learning environment—can dramatically improve outcomes in training programs and online training settings.
1. Arizona State University
Challenge: Traditional learning methods couldn’t keep struggling students engaged
Solution: The Knewton, a platform for adaptive learning and predictive analytics
Analytics Impact:
Tracked time spent, quiz patterns, and content interactions
Personalized lessons in real time
Helped students stay on track and improve performance (pass rates increased from 66% to 75%, and withdrawal rates decreased by 56%)
2. Carnegie Mellon
Challenge: Low engagement and poor math performance with traditional methods
Solution: Decimal Point, an adaptive digital game to teach decimals in middle school
Analytics Impact:
Monitored how students interacted with game tasks
Adapted feedback and challenge level
Improved engagement and learning outcomes
3. Stanford SMILE
Challenge: Lack of critical thinking development and real-time insights
Solution: Mobile LMS for underserved classrooms
Analytics Impact:
Tracked student-created questions, peer feedback, and response quality
Gave teachers real-time visibility into learning
Fostered deeper thinking and active participation
Sources:
Brief survey of analytics in K–12 and higher education
Bruce M. McLaren
Stanford Mobile Inquiry-based Learning Environment
So yes—learning management system analytics is powerful. But it can also be ambiguous, and this ambiguity lies in the fact that implementing analytics tools isn’t like choosing presents for your dearest friends: the more, the better. Quite the opposite.
Each block of features requires a respectable budget, not to mention time. Hence the questions:
Which metrics do you need?
Which are mandatory, and which are desirable?
The answers lie in two dimensions:
The stage of your learning platform life. Some analytics metrics you need right at the start, while others can be added as soon as your project scales up.
The industry. A feature like time-on-task, which measures how much time learners spend on specific tasks or assessments, is essential in formal education, where it helps identify learning bottlenecks and support knowledge retention. However, in fast-paced business environments, where speed and completion are the main priorities, this metric may offer less practical value, especially in large-scale online training initiatives.
In this article, we’ll explore both. The blog post body is focused more on the differentiation between basic and extended analytical features and the need for them at different stages of MLS development.
If your interest lies in choosing the right features for a specific industry, don’t miss the strategic checklist you’ll find at the end of this blog post.
