As per the celebrated theory of the Forgetting Curve by Herman Ebbinghaus, your workforce would simply forget 70% of what’s learned during a training program in just a few hours. How do we, then, achieve a sustainable Training ROI?
Well, this is where Microlearning backed by the concept of Spaced Repetition plays a role in refreshing and reviewing/reiterating knowledge items for your workforce!
The purpose of microlearning is to train and to be a dependable long-term memory enforcing tool! No wonder, the progressive organizations world-wide are increasingly adopting the microlearning approach to supplement, reinforce, augment and remediate their training initiatives.
It’s very important for you to know one thing:
For microlearning to succeed, you need a platform that’s terrific in implementing the concept of Spaced Repetition. MaxLearn uses this concept optimally to help you build a great learning organisation in the real sense. The aim: making your workforce functionally efficient & effective, besides turning them into a core strength of your business!
Why Spaced Repetition Matters?
Researchers like Ebbinghaus, Murre and Dros have shown us the exponential nature of ‘forgetting’. It’s a big problem when information/knowledge learned by your employees is lost over time. This normally happens when there is no attempt to retain it. Microlearning enabled by the concept of Spaced Repetition solves this problem. The Spaced Repetition principle helps build a strong long-term memory of on-the-job information/knowledge and skills.
The Spaced Repetition principle indicates that learning improves with repeated exposure and reiterations. A learner’s memory of the learned information/knowledge decays with delay in reiteration since the last exposure. A gap in reiterating or refreshing a certain information/knowledge item leads to memory decay.
Spaced Repetition boosts the ‘Probability of Recall’
For a high Training ROI, you need an inbuilt algorithm in your microlearning platform that helps your deliver a high ‘probability of recall’.
With Spaced Repetition, the probability of recall goes up by:
- providing repeated exposure to learning materials,
- keeping a tab on the elapsed time since their last review/reiteration, and
- repeating periodically & adequately based on the item difficulty as per each learner’s needs.
MaxLearn platform optimally utilizes the concept of Spaced Repetition to help your learner learn, retain and recall information or knowledge/skills. This leads to mastery over one’s job skills.
The big advantage with the Spaced Repetition algorithm applied by MaxLearn is:
- it’s based on the findings of how human memory works to facilitate long-term memory retention,
- it triggers cognitively-motivated scheduling of Spaced Repetition-i.e. ‘reiteration’ and ‘reviews’ are spaced over time.
MaxLearn’s Spaced Repetition: The Basics
MaxLearn’s Spaced Repetition algorithm uses the learning data optimally per epoch (one complete cycle of training) to maximize memory retention.
The algorithm is based on each learner’s unique goals as per one’s job-requirements, training needs, and learning-curve progress. MaxLearn’s Spaced Repetition algorithm churns-out the most efficient and optimum ‘total number of iterations’ in each complete cycle of training you plan.
This enables your learner to excel in one’s ability to retain, recall and use the training information without forgetting it. Spaced Repetition done right, and just-in-time makes learning easier, and not boring!
To give you maximum benefit, MaxLearn platform goes with the logic that any learning would be more effective when training starts with easier concepts and gradually advances to more difficult ones.
The AI edge of MaxLearn also helps you optimally organise your learning materials, questions, quizzes, games etc.
MaxLearn’s Spaced Repetition: Based on Brain Memory Model
MaxLearn’s Spaced Repetition algorithm is built upon the Brain Memory model. This model helps arrive at the precise number of reviews/reiterations required for Spaced Repetitions.
As per the Brain Memory Model, the recalling ability of a previously seen item depends on 3 things:
- the level of difficulty of the knowledge,
- delay since last review/repetition/reiteration of this knowledge item, and
- the strength of your learner’s memory.
The relation between these indicators and memory retention is used to calculate when to repeat or reiterate a knowledge item. The ‘estimated time of forgetting’ is calculated, and a ‘review/reiteration’ (Spaced Repetition) is scheduled for the learner before she/he forgets the information/knowledge item.
The recall indicators used for doing this effectively are:
- First and last review (reiterations) of a knowledge item,
- Keeping a tab on the ‘Recall point’ of a learner. This makes the probability of recalling a knowledge item high,
- Avoiding delays in reviews/repetitions, and
- Arranging reviews/repetition depending on the ‘level of difficulty’ of the knowledge item.
‘Repeat before Forgetting’ (RbF) Algorithm of MaxLearn
MaxLearn platform reviews the difficult and forgotten instances more frequently than the easily recalled instances using the inbuilt Repeat before Forgetting Algorithm (RbF). The algorithm supports learning and minimizes Ebbinghaus’ forgetting curve (memory decay).
MaxLearn’s adaptive microlearning platform specifically caters to the current understanding level of each learner.
While doing so, the RbF algorithm of Spaced Repetition first offers the easy knowledge items to a learner before exposing her/him to more complex items. The feature of individual learning goals in MaxLearn complements this Spaced Repetition algorithm.
The RbF algorithm of MaxLearn dynamically monitors every learner’s ‘recall confidence’. Based on this, the timing of spaced repetition algorithm is determined. This helps maintain the flexibility to shorten or delay the reviews/reiterations of each learner. How this works:
The inbuilt ‘scheduler’ in MaxLearn’s Repeat before Forgetting algorithm takes into account all the recall indicators of a learner to schedule review/reiteration intervals. This allows for spaced-repetition intervals (inter-repetitions) to be lengthened or shortened depending on an individual’s learning needs.
MaxLearn’s Analytics Suite Supports its ‘Spaced Repetition’ Algorithm
The effectiveness and efficiency of MaxLearn’s platform Spaced Repetition algorithm comes from the data points provided by its powerful analytics suite. The analytics capabilities of MaxLearn monitors and measures every learner’s interactions with the microlearning platform.
The Repeat before Forgetting (RbG) algorithm of MaxLearn draws its logic from the following data-points:
Competency status at each learning level per topic.
knowledge gap of each learner for each topic by checking if a learner has answered each question, test, assessment or quiz they ever participated in.
‘Time taken per question’, ‘active learner feedback’, and learner’s engagement (platform utilization).
A range of panels of the analytics suite like the engagement overview, learner engagement, assignment engagement, knowledge gaps, and learning card views.
Consequently, the laggards and slow learners are specifically taken care of with the Spaced Repetition algorithm.
The scheduling of Spaced Repetition is done optimally- i.e. just the right amount. This keeps the pain of overtraining away!
To conclude, the features of MaxLearn are specifically designed to be fast, fun and effective! The algorithm of Spaced Repetition does that for you, and helps you create a powerful learning organization. As a result, a great Training ROI with measurable and sustainable results becomes a reality!
