Learning analytics was supposed to make learning visible. Instead, it mostly made dashboards prettier.
Today, most learning platforms collect large volumes of data while offering very little insight into whether learning is actually happening. The problem is not a lack of data. It is the wrong definition of progress.
This article explains why most learning analytics fail, what a more meaningful alternative looks like, and why AI-native systems are beginning to expose signals that were previously invisible.
Why Most Learning Analytics Fail
Most learning analytics fail because they measure the wrong type of progress.
They track activity, completion, and interaction, but stop short of translating those signals into insight. What is produced is not understanding, but raw data, numbers that look precise yet offer little guidance for action.
Dashboards fill up. Decisions do not improve.
The result is a widespread illusion of visibility: the sense that learning is being monitored, without any real indication of whether it is improving.
The Normalization of Completion as a Metric
Completion rate is one of the most commonly used learning metrics today. It is also one of the least meaningful.
Completion has become dominant not because it reflects learning, but because it is easy to measure. A video either ends or it does not. A module is either marked complete or left unfinished.
This simplicity has led to a dangerous normalization. Completion is often treated as a proxy for understanding, progress, or competence, despite having no inherent relationship with any of them.
A learner can complete content without understanding it. They can understand content without completing it. The metric remains the same.
When completion becomes the primary signal, learning systems optimize for finishing, not for learning.
Data Without Insight Is Not Analytics
Collecting data is not the same as generating insight.
Many learning analytics systems present raw behavioral data, time spent, clicks, completion percentages, without answering the questions that actually matter. What changed in the learner’s understanding? Where did confusion persist? What kind of engagement led to learning, and what kind did not?
Without interpretation, data remains inert. It does not guide design decisions, pedagogical choices, or meaningful intervention. It merely documents surface-level behavior.
Analytics should inform action. When they do not, they become noise.
Learning Is Cognitive, Not Behavioral
The core limitation of traditional learning analytics is that they observe behavior while learning itself is cognitive.
Learning happens internally. It involves the construction, revision, and stabilization of mental models. Behavioral traces, clicks, views, completions, are at best indirect signals of that process.
This does not mean behavioral data is useless. It means it must be interpreted in context, rather than treated as evidence on its own.
Measuring behavior without modeling cognition leads to false confidence. It creates systems that are active, measurable, and inefficient at producing understanding.
What a Cognitive Signal Actually Is
A cognitive signal is not a single data point. It is an inferred indication that learning is occurring.
It emerges when engagement leads to measurable change: improved explanation, reduced misconception recurrence, stronger recall stability, or successful transfer across contexts. In other words, engagement matters only when it produces learning.
Cognitive signals are probabilistic, not absolute. They do not claim certainty. They provide direction.
This is why they are difficult to surface without AI. Static rules and predefined pathways cannot interpret these patterns at scale. AI-native systems can.
The Cost of Measuring the Wrong Things
When learning systems measure the wrong metrics, they drive the wrong decisions.
Designers optimize for completion instead of comprehension. Educators are incentivized to simplify rather than deepen. Learners are rewarded for speed rather than understanding.
Over time, this erodes learning quality. Content becomes thinner. Struggle is removed rather than supported. Superficial engagement replaces meaningful effort.
Bad analytics do not just misrepresent learning. They actively distort it.
What Learning Analytics Should Become
Learning analytics should move away from vanity metrics and toward depth and insight.
This does not mean abandoning measurement. It means redefining it. Analytics should help answer whether understanding is increasing, where it breaks down, and how learning experiences can adapt in response.
AI-native learning systems make this shift possible by treating analytics as a core function of the learning process, not as an afterthought layered onto content delivery. Some emerging platforms, such as SceneSnap, are exploring this approach by designing analytics around cognition rather than completion.
The technology will evolve. The principle should not.