Future of the Higher Education Ecosystem

by: Dan Genduso

In 2030, we will be migrating towards an education ecosystem that delivers personalized learning — learning based on user preferences and needs — that is consumed in small chunks throughout a person’s lifetime, while being combined with task-based work to validate mastery.

Identity or Profile as a Service: People will start to create, own, and manage their personal data, leading to the development of robust individual profiles that education service providers are able to consume as a service. These profiles will contain all kinds of data about the person, such as learning preferences, learning history, work history, personality traits, hard skills, and soft skills. By providing these profiles to education institutions, services, and peers as a service, individuals will be able to receive a highly focused and personalized learning experience, while also potentially earning money for the data that he/she provides.

Digital Assets: We should expect to see a shift in the way degrees, and newly created educational assets, start to be managed. Once earned, the degree should be turned into a digital asset, and that digital asset should be assigned ownership to the student via the blockchain. Once ownership is assigned, that degree will become a part of the student’s profile, which will validate the individual’s mastery, while also eliminating the need for education background checks. Educational institutions and services should then start creating additional sub-degree assets, which are at granular skills and sub-skill levels, and assign ownership of these assets immediately after a student tests out and validates mastery of a specific skill or activity. This creates a “living degree” that evolves throughout a person’s life.

Learning Paths: Learning paths will be created to teach people how to do things, such as starting a company, or converting a lesson plan into a workflow that students can follow at their own pace. These workflows will be created by a decentralized group of individuals, and the workflow will be broken down into milestones, activities, tasks, and sub-tasks. As a user completes a task, activity, or milestone, then the creator will receive payment based on the level of completion. If a user leverages tasks, activities, or milestones from multiple learning path creators to complete an entire workflow, each creator will receive payment reflective of his/her contribution to that user’s personal learning path.

Skills and Tasks: Since learning paths will be broken down into granular tasks, we can start to align those tasks with the skills required to complete tasks in the peer-to-peer employment world. Once that alignment is made, the student can be assigned a paid, real world task to complete while verifying mastery of the specific task, activity, or milestone that he/she just completed within a learning path. This will create a tightly integrated internship that evolves with a student throughout his/her learning journey, providing an actual employment history of completed tasks that are stored to his/her profile. If a student stops a learning path early, his/her profile still reflects mastery of the skills and tasks completed.

Predictive Analytics: Analytics will be leveraged to determine what shifts are likely to happen in the marketplace, allowing students to put a visualization layer over their individual profile and the available learning paths to determine the best course of action to meet job demands in the market. This provides individuals a way to chart unique paths towards long-term goals, while ensuring that they are always moving in a direction where paid work will be available along the way.

Peer-to-Peer Ecosystem: There will be a shift in education that mirrors the shift in employment when 30–40% of jobs are lost to automation, meaning that people will be operating in a peer-to-peer ecosystem, working to meet the needs of other individuals in local, national, or global communities. Individually owned profiles will be the cornerstone of this capability, and machine learning will be used to start automating the interactions between individuals in the community. This shift will start to change the way education materials are created, as teams of capable individuals will swarm around needs for things like text books — parsing out sections within a decentralized community and pulling those contributions back together in a wiki for global access — and operating within the structure provided by a shell organization (Decentralized Autonomous Nation).

Global Education Database: By 2040, an entire economy will be established around creating education content. We will see books published in highly agile ways by a decentralized community, after which those books will be made available in wiki format as living text books for consumption around the world.