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Higher knowledge retention by training the brain as often as possible!
Have you ever tried to watch the rotating wheel of a speeding car up close on TV or in a movie?
At times, and under certain conditions, it may seem that the wheel is turning anti-clockwise, although the car is moving right or vice-versa!!
This observation "error" is scientifically attributed to the fact that the shutter frequency of the camera filming the wheel is not high enough to match the frequency of the rotating wheel!
Claude Shannon (1916 - 2001) and Harry Nyquist (1889 - 1976) studied similar phenomena from a scientific, signals-and-systems perspective. They concluded that if we try to capture snapshots (i.e. discrete samples) of a continuously varying signal (e.g. the angular position of the rotating wheel), the frequency of the snapshots needs to be at least twice as large as the largest frequency of the continuously varying signal, if the intention is to reconstruct the continuous signal from the captured samples. Their work has been known as the Shannon-Nyquist Theorem, and the errors in question were called "aliasing errors".
Image of Claude Shannon by [Jacobs, Konrad] via Wikimedia Commons, licensed under CC BY-SA 2.0 Germany.
When it comes to knowledge dissemination, we can model knowledge as a (continuously varying) signal, since our brain continues to work even during our sleep! This means that when we deliver knowledge, even in discrete chunks, at least part of this knowledge is typically retained in the learner's mind.
If we then try to assess the learner's knowledge retention, we would be trying to sample (i.e. take discrete snapshots of) of the knowledge signal developing in his/her mind. Accordingly, and based on the Shannon-Nyquist theorem, if our intention is to reconstruct the actual continuous knowledge signal that has been acquired/retained by the learner, from the discrete samples (i.e. assessments) that we've collected, the frequency of these snapshots (i.e. samples/assessments) needs to be at least twice as high as the highest frequency of the acquired signal. This means that the learner needs to undergo at least two assessments during the smallest "period" of the acquired knowledge signal.
This is so important, particularly when we realise that, as a signal, knowledge is not just a point in time; rather it is a pathway that travels with time. Consequently, knowledge evaluation is not supposed to be based on the mere difference between the desired and the actual/acquired knowledge at a particular point in time; rather, proper knowledge evaluation is to be based on the difference of the "knowledge areas" covered between the desired and acquired knowledge signals (please see above picture). If the reconstructed acquired signal is riddled with (aliasing) errors, then the knowledge evaluation will not obviously be error free!
In other words, the higher the sampling/assessment frequency the more accurate our reconstruction of the acquired knowledge signal and the more valid our knowledge evaluation.
On the other hand, we know from Control Theory that dynamic processes perform better in a closed-loop rather than an open-loop configuration, in terms of "steady-state errors" and "transient behaviour", regardless of the sampling frequency of their input signal. Accordingly, one way to ensure better performance of the human brain is to properly close the learning loop. This simply means we need to provide proper feedback for every assessment provided to the learner.
In summary, at least two learning strategies could be deduced from the above. Perhaps not surprisingly, these strategies remind us of two good old rules:
1️⃣ Boost the assessment frequency as much as possible - i.e. Practice Makes Perfect.
2️⃣ Properly close the learning loop - i.e. make sure you receive proper and regular feedback.
At CedarLink, these two strategies are an integral part of our Learning Philosophy.
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