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Achieving Personalized Learning at Scale

How to exponentially increase the capacity of expert educators


Intro

The podcast group a16oz recently published a fascinating conversation forecasting the future of education in light of the influence of EdTech and the COVID-19 virus. We highly recommend listening to the episode in its entirety (which you can find HERE), but one of the topics explored was achieving personal learning at scale.


In the podcast, guests Josh Kim (Dartmouth College) and David Deming (Harvard University) emphasize the importance personal examples and direct feedback to dramatically improve information retention (while this is likely self-evident, you can see validating research HERE).


The Problem

Unfortunately, personalized learning is traditionally non-scalable, as it necessitates a 1-1 or 1-few relationship, and 1 person only has 24 hours in a day, regardless of how quickly and efficiently they can engage with others. This leads to inequity in education, as more qualified experts often provide their time to those who can pay the most.


On the opposite end of the spectrum from personalized learning are MOOC's (Massive Open Online Courses - like a public YouTube video), which provide expert insight, yet without any personal interaction or feedback based on the student's understanding of the content. MOOC's are infinitely scalable and equitable since there is no marginal cost for giving one more person access to content after it is produced.


So, how can we achieve the higher retention of personalized learning, but still have the infinite scalability of MOOC's?



Solution

Instead of providing generic content to a crowd or customized content to a small group:

Provide a framework + incentives for a community to engage each other with feedback to create self-scalable personalized learning.

What about the expert?

The personalized feedback may not be as effective as an individual expert providing feedback, but research shows that the knowledge of a group averages toward correct information. Plus, an expert can review and approve or tweak feedback already given exponentially faster than providing the feedback themselves.


What type of framework?

A thorough framework for requesting feedback, as well as giving feedback, creates clarity and generates actionable insight. For example, when image-based feedback requests include details like the problem trying to be solved, adjectives that are attempting to be communicated, a specific aspect of the image that the feedback should focus on, etc, feedback can provide more targeted, helpful results.


And when giving feedback, requiring a focus on both specific examples of what works well and what can be improved creates a more well-rounded analysis.


What type of incentive?

An incentive system gamifying the experience, such as a marketplace credit system where feedback must be given in order for it to be received, minimizes an imbalance in feedback requests and feedback responses, creating self-sufficiency for the community.


Awarding badges or a ranking system for engagement - especially engagement on specific areas of expertise - incentivizes users in three ways:

  • Increased activity

  • Allows users to build their personal brand as they rank up as peer-reviewed experts

  • Drives users to provide feedback on what they know the most about


Example

At Creative Critique, we have created a scalable personalized learning platform for feedback on creative work. Check out our platform and the framework we have built (based on interviews from 150+ creative educators and professionals) to facilitate quality community interactions at: https://www.creativecritique.community/beta


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