The Forgetting Curve in Workplace Training: Why Your Teams Forget (and How to Fix It)

The forgetting curve describes how quickly people lose newly learned information when they don't review it. In Hermann Ebbinghaus's 1885 experiments, retention fell to about 34% after 24 hours and 25% after six days, which means most of what a training session teaches is gone within days unless it is reinforced.

For a company, that's an uncomfortable finding: the budget invested in training often delivers far less than expected. Not because trainers do a poor job, but because that's how human memory works. The research that identified the problem also identified the solutions, spaced repetition and active recall, and today they can be applied at scale.

Key takeaways

• The forgetting curve was formalized by German psychologist Hermann Ebbinghaus in 1885 and replicated by Murre and Dros in 2015.
• Forgetting is steepest in the first hours after learning, then slows to a low plateau.
• Longer or denser training does not fix it: what matters is what happens after the session.
• Spaced repetition and the testing effect are the two best-documented levers to counter forgetting.
• Short mobile quizzes make both levers realistic for frontline teams, in a few minutes a day.

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What is the forgetting curve?

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The forgetting curve is a graph of how quickly learned information fades from memory when it isn't reactivated. It shows a steep drop in the first hours, followed by a slower decline.

What did Ebbinghaus measure?

The curve was formalized in 1885 by German psychologist Hermann Ebbinghaus, who conducted the first quantitative experiments on memory. He memorized lists of nonsense syllables, then measured how long it took him to relearn them at different intervals. He was his own and only test subject.

His results: about 58% retention after twenty minutes, 44% after one hour, 34% after twenty-four hours, and 25% after six days. In other words, nearly two-thirds of unreviewed information disappears within a day. A modern single-subject replication by Murre and Dros, published in PLOS ONE (2015), produced a very similar curve.

These figures often circulate in rounded, heavily extrapolated forms, all the way to claims like "90% forgotten in a week" that appear nowhere in the original study. With meaningful content, grounded in a work context and connected to existing knowledge, the decline is less abrupt than with arbitrary syllables.

But the shape of the curve doesn't change: a steep drop in the first few hours, then a low plateau. It is this profile that should guide the design of any training program.

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Why do we forget so quickly after training?

We forget quickly because information heard only once leaves a fragile memory trace, which fades if it is never reactivated. Forgetting has nothing to do with the learner's intelligence or motivation. Consolidation into long-term memory requires repeated reactivation, at increasing intervals.

For your teams, the consequence is direct. A two-day session, however dense and interactive, only creates the starting conditions for learning. What is taught is not learned for good: it starts to fade as soon as people return to their jobs.

This mechanism hits frontline teams even harder. A store associate, a field technician or a delivery driver doesn't go back over their training content between tasks. Their fragmented workday also speeds up forgetting, because there are few natural opportunities to reactivate what they learned.

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Why don't traditional training programs solve it?

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Traditional programs don't solve the forgetting curve because they act on volume, while the problem is how learning is spread over time. Making training longer just produces more hours subject to the same rate of forgetting, and packing more into the program overloads working memory and reduces retention instead of improving it.

Traditional e-learning doesn't solve the problem either. A forty-five-minute module completed once is forgotten exactly like an in-person session with no follow-up. How long learning lasts depends on what happens next, and in most programs nothing happens next.

This becomes obvious as soon as a company measures something other than immediate satisfaction. End-of-session surveys assess how comfortable the experience was, not what remains three months later. A program with no plan for after the session cannot produce lasting retention, however good its content.

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The levers that make knowledge stick for good

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The two levers that make knowledge stick are spaced repetition and the testing effect. Both are well documented by cognitive science, and combined they form the most effective structure for making learning last.

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What is spaced repetition?

Spaced repetition is a learning technique that reviews information at increasing intervals, right when it starts to fade. Reviewing too early is pointless, because the information is still available. Reviewing too late means relearning from scratch.

A meta-analysis by Cepeda and colleagues in Psychological Bulletin (2006), covering 839 assessments from 317 experiments, confirmed the benefit of spacing and found that the optimal gap between reviews grows with how long you need to remember.

Calculating that interval for each learner is impossible to manage manually across an organization. Mobile learning platforms automate it, adjusting the frequency for each concept based on previous answers. We cover the topic in a dedicated article on spaced repetition in training.

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What is the testing effect, or active recall?

The testing effect is the finding that actively recalling information strengthens it more than rereading it. When learners search their memory, even if they can't find the answer right away, they consolidate the trace far more than if they simply saw it displayed.

According to Roediger and Karpicke (2006), students who were repeatedly tested on a passage recalled 61% of it one week later, compared with 40% for students who reread it. Yet rereading remains the most common form of review in workplace training, which makes this one of the easiest gaps to close.

These two levers don't compete with existing formats; they extend them. The table below summarizes what each one brings and how to put it into practice.

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Why is mobile the right format for reinforcement?

Mobile is the right format because spaced review has to be regular, short and built into the workday. Nobody reopens a thirty-minute module between two customers, but answering five questions in three minutes on the bus is doable, and repeated over several weeks it produces a real memory effect.

Engagement matters just as much as format. An effective reinforcement program creates interaction, instant feedback and sometimes friendly competition between colleagues, and it is this dynamic that brings learners back. Without engagement, spaced repetition remains a good idea in theory: the algorithm sends its reminders, and nobody opens them.

It is the same logic as training in just a few minutes a day rather than concentrating all the effort into a single day.

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What SPARTED brings to the table

SPARTED is a mobile learning platform designed to extend the impact of training into teams' daily work. Reinforcement campaigns can be rolled out in a few days, with no particular technical skills, using formats built for mobile: multiple-choice questions, short scenarios, targeted reminders.

Spaced repetition is built in: the algorithm adjusts the frequency of reminders learner by learner, based on their results. The Back Office and its analytics show which concepts have been mastered, which ones are not sticking and where each employee stands. To go further on measurement, see how to evaluate the ROI of a mobile learning program.

This approach doesn't replace your training; it extends it. The initial session keeps its role, which is to lay the foundations and build buy-in. What changes is that the learning approach that follows is designed to counter the forgetting curve.

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Frequently asked questions

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What is the forgetting curve in simple terms?

The forgetting curve shows how quickly information disappears from memory when it isn't reviewed. Formalized by Hermann Ebbinghaus in 1885, it shows a steep drop in the first few hours, followed by a slower decline.

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Who came up with the forgetting curve?

German psychologist Hermann Ebbinghaus, in 1885, based on the first quantitative experiments on memory. He memorized lists of nonsense syllables and measured his own retention at different intervals. Murre and Dros replicated his experiment in 2015 and obtained a similar curve.

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What percentage of training is forgotten?

In Ebbinghaus's protocol, retention drops to about 34% after twenty-four hours and 25% after six days. With meaningful work-related content, the decline is less abrupt, but the pattern stays the same: most of the loss happens in the first few days. Claims like "90% forgotten in a week" do not come from his study.

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How can you fight the forgetting curve?

With two research-backed levers: spaced repetition, which means reviewing information at increasing intervals, and the testing effect, which means actively recalling information rather than rereading it. Mobile learning makes it possible to apply both at scale.

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Does mobile learning really help people remember?

Yes, as long as it is short, regular and engaging. It is repetition over time that makes learning stick, not the device itself. A mobile module completed only once is forgotten like any other format.